244 lines
9.0 KiB
Python
244 lines
9.0 KiB
Python
#!/usr/bin/env python3
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"""
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从「知你客服8号」复制为「知你客服9号」，强化「摘要 + 检索」可用性：
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1. 上下文 code-build-context：更长近期轮次、注入 conversation_summary、向量结果 + 关键词从历史中捞相关句。
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2. 摘要路径 code-build-memory-value：在原有摘要分支上合并进完整 conversation_history（追加而非仅 2 条），并写回 conversation_summary。
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3. cache-update-summary：显式 value 为「memory」表达式，避免整包 input_data 写入 Redis。
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4. 向量写入：为每条 turn 带 metadata.user_id；引擎侧检索已按 user_id 过滤。
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需平台可登录；默认源 8 号 ID 为上次创建结果，可用 ZHINI_8_AGENT_ID 覆盖。
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部署后请重启 Celery/API 以加载引擎向量过滤逻辑。
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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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SOURCE_AGENT_ID = os.getenv("ZHINI_8_AGENT_ID", "d7b64bf6-c8e3-4dc7-befc-03a98d5ff741")
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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9号"
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NEW_DESC = (
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"在知你客服8号基础上强化摘要与检索："
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"远期要点写入 conversation_summary；"
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"当轮上下文含「近期对话 + 摘要 + 向量片段 + 关键词相关历史」；"
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"向量库写入带 user_id 元数据，引擎检索按用户隔离。"
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"仍依赖 MEMORY_PERSIST_DB_ENABLED 与固定 user_id。"
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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」「远期摘要」「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】用户说出或暗示姓名、昵称时，必须在 user_profile.name 保存；合并已有字段勿丢失；
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4）用户问「我叫什么」等时，必须依据 user_profile.name 与对话/摘要回答；已有 name 时禁止说「还不知道」；
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5）「远期摘要」概括更早话题；「相关历史」可能含向量命中或关键词命中的旧轮次，请结合使用。
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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！","user_profile":{"name":"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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远期摘要：{{memory.conversation_summary}}
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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。"""
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CODE_BUILD_CONTEXT = r"""left = input_data.get('left') or {}
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right = input_data.get('right') or []
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if not isinstance(right, list):
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right = []
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mem = left.get('memory') or {}
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hist = mem.get('conversation_history') or []
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if not isinstance(hist, list):
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hist = []
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summary = mem.get('conversation_summary') or ''
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recent_n = 16
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recent = hist[-recent_n:] if len(hist) > recent_n else hist
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recent_str = '\n'.join(f"{x.get('role', '')}: {x.get('content', '')}" for x in recent)
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vec_str = '\n'.join((rec.get('text') or rec.get('content') or '') for rec in right)
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query = (left.get('user_input') or left.get('query') or '').strip()
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older = hist[:-recent_n] if len(hist) > recent_n else []
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def _tok(s):
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s = str(s)
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ch = {c for c in s if '\u4e00' <= c <= '\u9fff'}
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wd = set(s.lower().replace('\n', ' ').split())
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return ch | wd
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qt = _tok(query) if query else set()
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scored = []
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for m in older:
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c = str(m.get('content', ''))
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if not c:
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continue
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sc = len(qt & _tok(c)) if qt else 0
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if sc > 0:
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scored.append((sc, str(m.get('role', '')), c[:240]))
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scored.sort(key=lambda x: -x[0])
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kw_lines = [f"{role}: {text}" for _, role, text in scored[:6]]
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kw_str = '\n'.join(kw_lines)
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relevant_str = vec_str.strip()
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if kw_str:
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if relevant_str:
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relevant_str = relevant_str + '\n---\n关键词相关历史：\n' + kw_str
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else:
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relevant_str = '关键词相关历史：\n' + kw_str
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result = {
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'user_input': left.get('user_input') or left.get('query') or '',
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'memory': {
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'user_profile': mem.get('user_profile') or {},
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'conversation_summary': summary,
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'relevant_from_retrieval': relevant_str,
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'recent_turns': recent_str,
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},
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'query': left.get('query') or '',
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'user_id': left.get('user_id'),
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}
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"""
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CODE_BUILD_MEMORY_VALUE = r"""left = input_data.get('left') or {}
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right_out = input_data.get('right') or {}
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summary = ''
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if isinstance(right_out, dict):
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summary = right_out.get('output') or right_out.get('result') or ''
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if not isinstance(summary, str):
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summary = str(summary or '')
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summary = summary.strip()
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mem = left.get('memory') or {}
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user_input = left.get('user_input') or left.get('query') or ''
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reply = left.get('right') or ''
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if isinstance(reply, dict):
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reply = reply.get('right') or reply.get('content') or str(reply)
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profile_update = left.get('user_profile_update') or {}
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if not isinstance(profile_update, dict):
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profile_update = {}
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user_profile = dict(mem.get('user_profile') or {}, **profile_update)
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ts = datetime.now().isoformat()
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old_hist = mem.get('conversation_history') or []
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if not isinstance(old_hist, list):
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old_hist = []
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new_hist = old_hist + [
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{'role': 'user', 'content': user_input, 'timestamp': ts},
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{'role': 'assistant', 'content': str(reply or ''), 'timestamp': ts},
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]
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max_len = 40
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if len(new_hist) > max_len:
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new_hist = new_hist[-max_len:]
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prev_sum = (mem.get('conversation_summary') or '').strip()
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conversation_summary = summary if summary else prev_sum
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memory_value = {
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'conversation_summary': conversation_summary,
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'conversation_history': new_hist,
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'user_profile': user_profile,
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'context': mem.get('context') or {},
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}
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result = {
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'memory': memory_value,
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'user_id': left.get('user_id'),
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'query': left.get('query'),
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'user_input': user_input,
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'right': reply,
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'user_profile_update': profile_update,
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}
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"""
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CODE_BUILD_TURN_FOR_VECTOR = r"""reply = input_data.get('right') or ''
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if isinstance(reply, dict):
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reply = reply.get('right') or reply.get('content') or str(reply)
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query = input_data.get('query') or ''
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user_id = str(input_data.get('user_id') or 'default')
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raw = (user_id + '\n' + str(query) + '\n' + str(reply)).encode('utf-8', errors='ignore')
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doc_id = 'turn_' + hashlib.sha256(raw).hexdigest()[:24]
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text = '用户：' + str(query) + '\n助手：' + str(reply)
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result = {
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'text': text,
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'user_id': user_id,
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'id': doc_id,
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'metadata': {'user_id': user_id},
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}
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"""
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def _patch_nodes(wf: dict) -> None:
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nodes = wf.get("nodes") or []
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for n in nodes:
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nid = n.get("id")
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if nid == "llm-unified":
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n.setdefault("data", {})["prompt"] = LLM_PROMPT
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elif nid == "code-build-context":
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n.setdefault("data", {})["code"] = CODE_BUILD_CONTEXT
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elif nid == "code-build-memory-value":
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n.setdefault("data", {})["code"] = CODE_BUILD_MEMORY_VALUE
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elif nid == "code-build-turn-for-vector":
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n.setdefault("data", {})["code"] = CODE_BUILD_TURN_FOR_VECTOR
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elif nid == "cache-update-summary":
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d = n.setdefault("data", {})
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d["value"] = "memory"
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elif nid == "transform-for-vector-upsert":
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m = n.setdefault("data", {}).setdefault("mapping", {})
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m["metadata"] = "{{left.metadata}}"
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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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_patch_nodes(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={"description": NEW_DESC, "workflow_config": wf},
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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：LLM 提示、code-build-context / memory-value / vector-turn、cache-update-summary.value、upsert.metadata")
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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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