126 lines
5.0 KiB
Python
126 lines
5.0 KiB
Python
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#!/usr/bin/env python3
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"""
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从「知你客服7号」复制为「知你客服8号」：在 7 号多轮记忆能力基础上，说明使用平台「永久记忆」
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（Cache user_memory_* 同步写入 MySQL persistent_user_memories，需 MEMORY_PERSIST_DB_ENABLED=true）。
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需本地平台已启动（默认 http://127.0.0.1:8037），账号可通过环境变量配置。
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用法:
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cd backend && .\\venv\\Scripts\\python.exe scripts/create_zhini_kefu_8.py
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"""
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from __future__ import annotations
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import json
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import os
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import sys
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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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# 默认从 7 号复制；也可通过环境变量指定
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SOURCE_AGENT_ID = os.getenv("ZHINI_7_AGENT_ID", "688c2c41-dcd1-4285-b193-6bed00c485c2")
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USER = os.getenv("PLATFORM_USERNAME", "admin")
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PWD = os.getenv("PLATFORM_PASSWORD", "123456")
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NEW_NAME = "知你客服8号"
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NEW_DESC = (
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"在知你客服7号基础上面向「永久记忆」：工作流仍为 user_memory_{user_id} 读写；"
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"引擎将记忆同步至 MySQL（跨 Redis TTL、服务重启仍保留）。"
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"调用时请固定传入 user_id；部署需开启 MEMORY_PERSIST_DB_ENABLED。"
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)
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LLM_PROMPT = """你是客服助手。根据「用户当前输入」「已知用户信息」「相关历史（检索）」和「最近几轮」完成：
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1）判断意图；
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2）生成一句自然、有帮助的回复；
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3）【强制】只要用户说出或暗示自己的姓名、昵称，必须在 user_profile 里用字段 name 保存，例如用户说「我叫王小明」则 JSON 必须包含 "user_profile":{"name":"王小明"}（若已有其它字段则合并，不要丢字段）；
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4）若用户问「我叫什么」「你还记得我名字吗」等，必须根据「已知用户信息」里的 user_profile.name 与对话历史回答；若已有 name 则禁止说「还不知道」。
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5）系统会在后台持久化用户画像与近期对话；请始终基于「已知用户信息」与「最近几轮」作答，避免与用户已提供信息矛盾。
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只输出一行合法 JSON，不要 markdown。格式示例：
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{"intent":"greeting","reply":"你好王小明！","user_profile":{"name":"王小明"}}
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用户输入：{{user_input}}
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已知用户信息：{{memory.user_profile}}
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相关历史（检索到的）：{{memory.relevant_from_retrieval}}
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最近几轮：{{memory.recent_turns}}
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要求：reply 简洁自然，200 字以内；user_profile 为对象，至少包含 name（当用户自我介绍时）。"""
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def _patch_cache_nodes_for_memory(wf: dict) -> None:
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"""为 Cache 节点设置更长对话窗口与较长 Redis TTL；真正永久存储由引擎写 MySQL。"""
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nodes = wf.get("nodes") or []
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for n in nodes:
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if n.get("type") != "cache":
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continue
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data = n.setdefault("data", {})
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op = data.get("operation", "get")
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if op == "set":
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data["max_history_length"] = 40
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data["ttl"] = 604800 # 7 天热缓存；冷数据仍可从 DB 拉回
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elif op == "get":
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data["ttl"] = 604800
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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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dup = requests.post(
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f"{BASE}/api/v1/agents/{SOURCE_AGENT_ID}/duplicate",
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headers=h,
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json={"name": NEW_NAME},
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timeout=30,
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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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print("已创建副本:", new_id, NEW_NAME)
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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 Agent 失败:", g.text, file=sys.stderr)
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return 1
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agent = g.json()
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wf = agent["workflow_config"]
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nodes = wf.get("nodes") or []
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for n in nodes:
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if n.get("id") == "llm-unified":
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n.setdefault("data", {})["prompt"] = LLM_PROMPT
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break
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_patch_cache_nodes_for_memory(wf)
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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={
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"description": NEW_DESC,
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"workflow_config": wf,
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},
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timeout=60,
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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[:800], file=sys.stderr)
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return 1
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print("已更新描述、llm-unified 提示词，并为 Cache 节点设置 max_history_length/ttl（可选）")
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print("Agent ID:", new_id)
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print(json.dumps({"id": new_id, "name": NEW_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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