270 lines
9.6 KiB
Python
270 lines
9.6 KiB
Python
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#!/usr/bin/env python3
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"""
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从「知你客服9号」复制为「知你客服10号」，改进记忆与连接策略：
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1. 在 json-parse 之后插入 code-identity-merge：把用户指定的「助手称呼」写入 memory.context.assistant_display_name
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（与 user_profile.name 区分）。
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2. code-build-context：向 LLM 注入 assistant_display_name。
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3. llm-unified 提示词：自我介绍优先用 assistant_display_name；user_profile.name 仅表示用户。
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4. condition-need-summary：history_count >= 2 即走摘要分支（原常为 >=4，summary 易长期为空）。
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需 Celery 已加载含 re/hashlib 注入的 workflow_engine（代码节点内勿写 import re）。
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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_9_AGENT_ID", "de5932d6-3c05-4b27-ab08-f6cb403ce4b9")
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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10号"
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NEW_DESC = (
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"在知你客服9号基础上：① memory.context.assistant_display_name 存助手对外称呼，与 user_profile.name（用户）分离；"
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"② 摘要分支 history_count>=2 更易生成 conversation_summary；"
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"③ 工作流在 json-parse 后增加 code-identity-merge 再进入抽取/写记忆。"
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)
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CODE_IDENTITY_MERGE = r"""mem = dict(input_data.get('memory') or {})
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ctx = dict(mem.get('context') or {})
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q = str(input_data.get('query') or input_data.get('user_input') or '').strip()
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for pat in (
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r'你的\s*名字\s*叫\s*([^\s，。！？,.!?]{1,32})',
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r'你\s*叫\s*(?!什么)([^\s，。！？,.!?]{1,32})',
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r'(?:客服|助手)\s*叫\s*([^\s，。！？,.!?]{1,32})',
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):
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m = re.search(pat, q)
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if not m:
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continue
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name = m.group(1).strip().strip('，。！？,.!?')
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if not name:
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continue
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if any(b in name for b in ('什么', '哪位', '谁', '啥')):
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continue
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ctx['assistant_display_name'] = name
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break
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mem['context'] = ctx
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out = dict(input_data)
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out['memory'] = mem
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result = out
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"""
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CODE_BUILD_CONTEXT_V10 = 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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ctx = mem.get('context') or {}
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if not isinstance(ctx, dict):
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ctx = {}
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assistant_name = str(ctx.get('assistant_display_name') or '').strip()
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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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'assistant_display_name': assistant_name,
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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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LLM_PROMPT_V10 = """你是客服助手。根据用户输入、用户画像、助手称呼、远期摘要、检索片段与最近对话生成回复。
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【称呼规则】
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- user_profile.name（及同类字段）仅表示「用户」的昵称/姓名。
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- memory.assistant_display_name 表示用户为你指定的「对外称呼」。若非空，用户问「你叫什么名字」「你是谁」时，须用该称呼自称（可带「客服助手」类前缀，但核心名须一致）；禁止忽略已保存的 assistant_display_name 改回默认虚构名。
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- 若 assistant_display_name 为空，可自称「客服助手」等通用名。
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【任务】
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1）判断意图；2）自然、有帮助的 reply（JSON 内一条字符串）；
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3）用户自我介绍姓名时写入 user_profile（如 name），勿把用户姓名写入 assistant_display_name；
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4）用户问「我叫什么」时依据 user_profile 与历史/摘要回答。
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只输出一行合法 JSON，不要 markdown。示例：
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{"intent":"chat","reply":"你好！","user_profile":{"name":"小明"}}
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用户输入：{{user_input}}
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用户画像：{{memory.user_profile}}
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助手对外称呼（用户指定，可能为空）：{{memory.assistant_display_name}}
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远期摘要：{{memory.conversation_summary}}
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相关历史（检索）：{{memory.relevant_from_retrieval}}
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最近几轮：{{memory.recent_turns}}
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要求：reply 200 字以内；user_profile 为对象。"""
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def _insert_identity_node_and_edges(wf: dict) -> None:
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nodes = wf.setdefault("nodes", [])
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edges = wf.setdefault("edges", [])
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if any(n.get("id") == "code-identity-merge" for n in nodes):
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return
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# 参考 json-parse 位置：在其右侧插入
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jx, jy = 2200, 400
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for n in nodes:
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if n.get("id") == "json-parse":
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pos = n.get("position") or {}
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jx = pos.get("x", jx) + 80
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jy = pos.get("y", jy)
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break
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nodes.append(
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{
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"id": "code-identity-merge",
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"type": "code",
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"position": {"x": jx, "y": jy},
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"data": {
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"label": "合并助手称呼到 context",
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"language": "python",
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"code": CODE_IDENTITY_MERGE,
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},
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}
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)
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new_edges = []
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removed = False
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for e in edges:
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if e.get("source") == "json-parse" and e.get("target") == "transform-extract-reply-and-profile":
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removed = True
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continue
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new_edges.append(e)
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if not removed:
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print("警告: 未找到 json-parse -> transform-extract-reply-and-profile 的边，仍追加新边", file=sys.stderr)
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new_edges.append(
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{
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"id": "e11a-identity",
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"source": "json-parse",
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"target": "code-identity-merge",
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"sourceHandle": "right",
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"targetHandle": "left",
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}
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)
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new_edges.append(
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{
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"id": "e11b-identity",
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"source": "code-identity-merge",
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"target": "transform-extract-reply-and-profile",
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"sourceHandle": "right",
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"targetHandle": "left",
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}
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)
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wf["edges"] = new_edges
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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_V10
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elif nid == "code-build-context":
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n.setdefault("data", {})["code"] = CODE_BUILD_CONTEXT_V10
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elif nid == "condition-need-summary":
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d = n.setdefault("data", {})
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c = d.get("condition", "")
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if "history_count" in c and ">=" in c:
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d["condition"] = "{history_count} >= 2"
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else:
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d["condition"] = "{history_count} >= 2"
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elif nid == "code-identity-merge":
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n.setdefault("data", {})["code"] = CODE_IDENTITY_MERGE
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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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_insert_identity_node_and_edges(wf)
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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=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[:800], file=sys.stderr)
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return 1
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print("已更新：identity 节点与边、摘要阈值>=2、上下文与 LLM 提示")
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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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