0608161c82
补齐平台模板与场景 DSL、预算控制、执行看板和企业场景脚本，增强 Windows 启动/迁移与前端代理和聊天会话记忆，修复执行创建阶段 500 与异步链路排障体验。 Made-with: Cursor
303 lines
11 KiB
Python
303 lines
11 KiB
Python
#!/usr/bin/env python3
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"""
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从「知你客服14号」复制为「知你客服15号」：
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- **工具**：与 14 号相同（平台当前全量内置工具）。
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- **可持续执行**：在 LLM 节点写入 **max_tool_iterations**（默认 28），引擎在同一轮执行内允许多次
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「模型 → 工具 → 模型 → …」迭代，便于长链路干活（读文件→写文件→再校验等），而非只调一次工具就结束。
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- **提示词**：强调「持续反馈、多步工具链、任务完成判定」及末行 JSON 可选字段 `task_complete` / `progress_report` 等；
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若单次无法跑完，引导用户下轮「继续」并依赖会话记忆接续。
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用法:
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cd backend && .\\venv\\Scripts\\python.exe scripts/create_zhini_kefu_15.py
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环境变量: PLATFORM_BASE_URL, PLATFORM_USERNAME, PLATFORM_PASSWORD,
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SOURCE_AGENT_NAME（默认 知你客服14号）, TARGET_NAME（默认 知你客服15号）
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"""
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from __future__ import annotations
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import copy
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import json
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import os
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import sys
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from collections import defaultdict
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from typing import Any, Dict, List, Optional, Tuple
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import requests
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BASE = os.getenv("PLATFORM_BASE_URL", "http://127.0.0.1:8037").rstrip("/")
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USER = os.getenv("PLATFORM_USERNAME", "admin")
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PWD = os.getenv("PLATFORM_PASSWORD", "123456")
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SOURCE_NAME = os.getenv("SOURCE_AGENT_NAME", "知你客服14号")
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TARGET_NAME = os.getenv("TARGET_NAME", "知你客服15号")
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TOOLS_V15: List[str] = [
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"http_request",
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"file_read",
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"file_write",
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"text_analyze",
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"datetime",
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"math_calculate",
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"system_info",
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"json_process",
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"database_query",
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"adb_log",
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]
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# 与引擎 workflow_engine 中读取的字段一致（上限 64）
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DEFAULT_MAX_TOOL_ITERATIONS = 28
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PROMPT_V15_MARKER = "【知你客服 15 号 · 可持续任务执行】"
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PROMPT_V15_EXTRA = f"""
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{PROMPT_V15_MARKER}
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【角色】你是**可持续执行型**客服助手：面对需要多步工具配合的任务（如：查路径 → 读配置 → 写文件 → 再读回校验），应在**同一轮对话的一次执行**内，**连续使用工具**并根据返回结果决定下一步，直到任务完成或明确受阻；不要只做一次工具调用就结束。
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【与 14 号的关系】继承 14 号全部内置工具与纪律；**工具列表未删减**，平台侧已为 15 号提高**单次执行内工具迭代次数**（见节点 `max_tool_iterations`）。
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【执行策略】
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1. **多步工具链**：先 `system_info` 确认工作区再 `file_write`；需要外部信息再 `http_request`；需要数据再 `database_query`（仅 SELECT）。每一步根据上一步真实返回再决策。
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2. **持续反馈**：在最终自然语言中说明**已做步骤**与**当前结果**；勿编造工具返回。
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3. **何时停**：目标达成 → 在末行 JSON 中标明完成；缺用户输入/权限/环境 → 清楚说明缺什么。
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4. **单次装不下时**：在 `reply` 中说明进度，并建议用户**下一轮发送「继续」**；可把未完成要点写入 `user_profile` 或依赖会话记忆中的 `conversation_history` 衔接（勿用空 JSON 覆盖画像）。
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【末行 JSON（单行）扩展字段（推荐）】
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在原有 `intent`、`reply`、`user_profile` 基础上，可增加：
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- `task_complete`: boolean，本任务是否已彻底完成；
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- `progress_report`: string，本轮已完成步骤的简要清单；
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- `continuation_hint`: string，若 `task_complete` 为 false，提示用户下一句怎么说（如「继续」「补充 xxx」）。
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仍须以 **一行合法 JSON** 结尾，勿用 markdown 代码围栏。
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【纪律】继承 14 号：勿刷屏 DSML；`database_query` 仅 SELECT；`file_write` 同轮勿无故重复写入同一文件除非必要。
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"""
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def _sanitize_edges(edges: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
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seen: set = set()
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out: List[Dict[str, Any]] = []
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for e in edges or []:
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s, t = e.get("source"), e.get("target")
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if not s or not t:
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continue
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if s == t:
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continue
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key = (s, t)
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if key in seen:
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continue
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seen.add(key)
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ne = dict(e)
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ne["sourceHandle"] = "right"
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ne["targetHandle"] = "left"
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if not ne.get("id"):
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ne["id"] = f"edge_{s}_{t}"
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out.append(ne)
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return out
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def _find_start_node_ids(nodes: List[Dict[str, Any]]) -> List[str]:
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ids: List[str] = []
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for n in nodes or []:
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nid = n.get("id") or ""
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nt = (n.get("type") or (n.get("data") or {}).get("type") or "").lower()
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if nt == "start" or nid in ("start", "start-1") or str(nid).startswith("start-"):
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ids.append(nid)
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return ids
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def _compute_ranks(
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nodes: List[Dict[str, Any]], edges: List[Dict[str, Any]]
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) -> Dict[str, int]:
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node_ids = [n["id"] for n in nodes if n.get("id")]
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start_ids = _find_start_node_ids(nodes)
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incoming: Dict[str, int] = {nid: 0 for nid in node_ids}
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for e in edges:
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s, t = e.get("source"), e.get("target")
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if not s or not t or s == t:
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continue
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if t in incoming:
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incoming[t] += 1
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if not start_ids:
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start_ids = [nid for nid in node_ids if incoming.get(nid, 0) == 0] or ([node_ids[0]] if node_ids else [])
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rank: Dict[str, int] = {s: 0 for s in start_ids}
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nmax = max(len(nodes), 8)
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for _ in range(nmax + 5):
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updated = False
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for e in edges:
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s, t = e.get("source"), e.get("target")
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if not s or not t or s == t:
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continue
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if s not in rank:
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continue
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nv = rank[s] + 1
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if t not in rank or rank[t] < nv:
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rank[t] = nv
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updated = True
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if not updated:
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break
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max_r = max(rank.values(), default=0)
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for nid in node_ids:
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if nid not in rank:
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rank[nid] = max_r + 1
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max_r += 1
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return rank
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def _apply_layered_positions(nodes: List[Dict[str, Any]], ranks: Dict[str, int]) -> None:
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layers: Dict[int, List[str]] = defaultdict(list)
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for nid, r in ranks.items():
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layers[r].append(nid)
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for r in layers:
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layers[r].sort()
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x0, y0 = 80.0, 140.0
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x_step = 300.0
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y_step = 110.0
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for r in sorted(layers.keys()):
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ids = layers[r]
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nlen = len(ids)
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y_base = y0 - (nlen - 1) * y_step / 2.0
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for j, nid in enumerate(ids):
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for node in nodes:
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if node.get("id") != nid:
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continue
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pos = node.setdefault("position", {})
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pos["x"] = x0 + r * x_step
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pos["y"] = y_base + j * y_step
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break
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def improve_workflow_layout_and_edges(wf: Dict[str, Any]) -> Tuple[int, int]:
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nodes = wf.get("nodes") or []
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raw_edges = wf.get("edges") or []
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loops = sum(
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1
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for e in raw_edges
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if e.get("source") and e.get("target") and e.get("source") == e.get("target")
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)
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clean = _sanitize_edges(raw_edges)
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removed_dup = len(raw_edges) - len(clean) - loops
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wf["edges"] = clean
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ranks = _compute_ranks(nodes, clean)
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_apply_layered_positions(nodes, ranks)
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return loops, max(0, removed_dup)
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def _patch_llm_unified(wf: dict, base_prompt: Optional[str] = None) -> None:
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for n in wf.get("nodes") or []:
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if n.get("id") != "llm-unified":
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continue
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d = n.setdefault("data", {})
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prompt = base_prompt if base_prompt else d.get("prompt") or ""
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if PROMPT_V15_MARKER not in prompt:
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prompt = (prompt.rstrip() + "\n" + PROMPT_V15_EXTRA).strip()
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d["prompt"] = prompt
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d["enable_tools"] = True
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d["tools"] = list(TOOLS_V15)
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d["selected_tools"] = list(TOOLS_V15)
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d["max_tool_iterations"] = DEFAULT_MAX_TOOL_ITERATIONS
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return
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print("警告: 未找到节点 llm-unified", file=sys.stderr)
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def _find_agent_id_by_name(h: Dict[str, str], name: str) -> Optional[str]:
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r = requests.get(f"{BASE}/api/v1/agents", params={"search": name, "limit": 50}, headers=h, timeout=30)
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if r.status_code != 200:
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return None
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for a in r.json() or []:
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if a.get("name") == name:
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return a.get("id")
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return None
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def main() -> int:
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r = requests.post(
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f"{BASE}/api/v1/auth/login",
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data={"username": USER, "password": PWD},
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headers={"Content-Type": "application/x-www-form-urlencoded"},
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timeout=15,
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)
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if r.status_code != 200:
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print("登录失败:", r.status_code, r.text[:500], file=sys.stderr)
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return 1
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token = r.json().get("access_token")
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if not token:
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print("无 access_token", file=sys.stderr)
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return 1
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h = {"Authorization": f"Bearer {token}", "Content-Type": "application/json"}
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src_id = _find_agent_id_by_name(h, SOURCE_NAME)
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if not src_id:
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print(f"未找到源 Agent: {SOURCE_NAME}", file=sys.stderr)
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return 1
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existing = _find_agent_id_by_name(h, TARGET_NAME)
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if existing:
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print("已存在", TARGET_NAME, "-> 仅更新工作流", existing)
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new_id = existing
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g = requests.get(f"{BASE}/api/v1/agents/{new_id}", headers=h, timeout=30)
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if g.status_code != 200:
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print("读取失败:", g.text, file=sys.stderr)
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return 1
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agent = g.json()
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else:
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dup = requests.post(
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f"{BASE}/api/v1/agents/{src_id}/duplicate",
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headers=h,
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json={"name": TARGET_NAME},
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timeout=60,
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)
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if dup.status_code != 201:
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print("复制失败:", dup.status_code, dup.text[:800], file=sys.stderr)
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return 1
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new_id = dup.json()["id"]
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agent = dup.json()
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print("已创建副本:", new_id, TARGET_NAME)
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wf = copy.deepcopy(agent["workflow_config"])
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loops, dup_edges = improve_workflow_layout_and_edges(wf)
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print(f"连线整理: 去掉自环 {loops} 条, 合并重复边 {dup_edges} 条")
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g2 = requests.get(f"{BASE}/api/v1/agents/{src_id}", headers=h, timeout=30)
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base_prompt = None
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if g2.status_code == 200:
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try:
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for n in g2.json().get("workflow_config", {}).get("nodes") or []:
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if n.get("id") == "llm-unified":
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base_prompt = (n.get("data") or {}).get("prompt")
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break
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except Exception:
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pass
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_patch_llm_unified(wf, base_prompt=base_prompt)
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desc = (
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"知你客服15号：在14号全量工具基础上，强调可持续多步执行；"
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f"llm-unified 配置 max_tool_iterations={DEFAULT_MAX_TOOL_ITERATIONS}，"
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"单次执行内可多轮工具调用直至任务完成或明确需用户继续；输出单行 JSON，可含 task_complete/progress_report。"
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)
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up = requests.put(
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f"{BASE}/api/v1/agents/{new_id}",
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headers=h,
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json={"description": desc, "workflow_config": wf},
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timeout=120,
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)
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if up.status_code != 200:
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print("更新失败:", up.status_code, up.text[:1200], file=sys.stderr)
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return 1
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print("已写入工具:", ", ".join(TOOLS_V15))
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print(f"max_tool_iterations: {DEFAULT_MAX_TOOL_ITERATIONS}")
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print("Agent ID:", new_id)
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print(json.dumps({"id": new_id, "name": TARGET_NAME}, ensure_ascii=False))
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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