#!/usr/bin/env python3
"""
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：

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。
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。
3. cache-update-summary：显式 value 为「memory」表达式，避免整包 input_data 写入 Redis。
4. 向量写入：为每条 turn 带 metadata.user_id；引擎侧检索已按 user_id 过滤。

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。
部署后请重启 Celery/API 以加载引擎向量过滤逻辑。
"""
from __future__ import annotations

import json
import os
import sys

import requests

BASE = os.getenv("PLATFORM_BASE_URL", "http://127.0.0.1:8037").rstrip("/")
SOURCE_AGENT_ID = os.getenv("ZHINI_8_AGENT_ID", "d7b64bf6-c8e3-4dc7-befc-03a98d5ff741")
USER = os.getenv("PLATFORM_USERNAME", "admin")
PWD = os.getenv("PLATFORM_PASSWORD", "123456")

NEW_NAME = "知你客服9号"
NEW_DESC = (
    "在知你客服8号基础上强化摘要与检索："
    "远期要点写入 conversation_summary；"
    "当轮上下文含「近期对话 + 摘要 + 向量片段 + 关键词相关历史」；"
    "向量库写入带 user_id 元数据，引擎检索按用户隔离。"
    "仍依赖 MEMORY_PERSIST_DB_ENABLED 与固定 user_id。"
)

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：
1）判断意图；
2）生成一句自然、有帮助的回复；
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；
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」；
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。

只输出一行合法 JSON，不要 markdown。格式示例：
{"intent":"greeting","reply":"你好！","user_profile":{"name":"小明"}}

用户输入：{{user_input}}
已知用户信息：{{memory.user_profile}}
远期摘要：{{memory.conversation_summary}}
相关历史（检索到的）：{{memory.relevant_from_retrieval}}
最近几轮：{{memory.recent_turns}}

要求：reply 简洁自然，200 字以内；user_profile 为对象。"""

CODE_BUILD_CONTEXT = r"""left = input_data.get('left') or {}
right = input_data.get('right') or []
if not isinstance(right, list):
    right = []
mem = left.get('memory') or {}
hist = mem.get('conversation_history') or []
if not isinstance(hist, list):
    hist = []
summary = mem.get('conversation_summary') or ''
recent_n = 16
recent = hist[-recent_n:] if len(hist) > recent_n else hist
recent_str = '\n'.join(f"{x.get('role', '')}: {x.get('content', '')}" for x in recent)
vec_str = '\n'.join((rec.get('text') or rec.get('content') or '') for rec in right)
query = (left.get('user_input') or left.get('query') or '').strip()
older = hist[:-recent_n] if len(hist) > recent_n else []


def _tok(s):
    s = str(s)
    ch = {c for c in s if '\u4e00' <= c <= '\u9fff'}
    wd = set(s.lower().replace('\n', ' ').split())
    return ch | wd


qt = _tok(query) if query else set()
scored = []
for m in older:
    c = str(m.get('content', ''))
    if not c:
        continue
    sc = len(qt & _tok(c)) if qt else 0
    if sc > 0:
        scored.append((sc, str(m.get('role', '')), c[:240]))
scored.sort(key=lambda x: -x[0])
kw_lines = [f"{role}: {text}" for _, role, text in scored[:6]]
kw_str = '\n'.join(kw_lines)
relevant_str = vec_str.strip()
if kw_str:
    if relevant_str:
        relevant_str = relevant_str + '\n---\n关键词相关历史：\n' + kw_str
    else:
        relevant_str = '关键词相关历史：\n' + kw_str
result = {
    'user_input': left.get('user_input') or left.get('query') or '',
    'memory': {
        'user_profile': mem.get('user_profile') or {},
        'conversation_summary': summary,
        'relevant_from_retrieval': relevant_str,
        'recent_turns': recent_str,
    },
    'query': left.get('query') or '',
    'user_id': left.get('user_id'),
}
"""

CODE_BUILD_MEMORY_VALUE = r"""left = input_data.get('left') or {}
right_out = input_data.get('right') or {}
summary = ''
if isinstance(right_out, dict):
    summary = right_out.get('output') or right_out.get('result') or ''
if not isinstance(summary, str):
    summary = str(summary or '')
summary = summary.strip()
mem = left.get('memory') or {}
user_input = left.get('user_input') or left.get('query') or ''
reply = left.get('right') or ''
if isinstance(reply, dict):
    reply = reply.get('right') or reply.get('content') or str(reply)
profile_update = left.get('user_profile_update') or {}
if not isinstance(profile_update, dict):
    profile_update = {}
user_profile = dict(mem.get('user_profile') or {}, **profile_update)
ts = datetime.now().isoformat()
old_hist = mem.get('conversation_history') or []
if not isinstance(old_hist, list):
    old_hist = []
new_hist = old_hist + [
    {'role': 'user', 'content': user_input, 'timestamp': ts},
    {'role': 'assistant', 'content': str(reply or ''), 'timestamp': ts},
]
max_len = 40
if len(new_hist) > max_len:
    new_hist = new_hist[-max_len:]
prev_sum = (mem.get('conversation_summary') or '').strip()
conversation_summary = summary if summary else prev_sum
memory_value = {
    'conversation_summary': conversation_summary,
    'conversation_history': new_hist,
    'user_profile': user_profile,
    'context': mem.get('context') or {},
}
result = {
    'memory': memory_value,
    'user_id': left.get('user_id'),
    'query': left.get('query'),
    'user_input': user_input,
    'right': reply,
    'user_profile_update': profile_update,
}
"""

CODE_BUILD_TURN_FOR_VECTOR = r"""reply = input_data.get('right') or ''
if isinstance(reply, dict):
    reply = reply.get('right') or reply.get('content') or str(reply)
query = input_data.get('query') or ''
user_id = str(input_data.get('user_id') or 'default')
raw = (user_id + '\n' + str(query) + '\n' + str(reply)).encode('utf-8', errors='ignore')
doc_id = 'turn_' + hashlib.sha256(raw).hexdigest()[:24]
text = '用户：' + str(query) + '\n助手：' + str(reply)
result = {
    'text': text,
    'user_id': user_id,
    'id': doc_id,
    'metadata': {'user_id': user_id},
}
"""


def _patch_nodes(wf: dict) -> None:
    nodes = wf.get("nodes") or []
    for n in nodes:
        nid = n.get("id")
        if nid == "llm-unified":
            n.setdefault("data", {})["prompt"] = LLM_PROMPT
        elif nid == "code-build-context":
            n.setdefault("data", {})["code"] = CODE_BUILD_CONTEXT
        elif nid == "code-build-memory-value":
            n.setdefault("data", {})["code"] = CODE_BUILD_MEMORY_VALUE
        elif nid == "code-build-turn-for-vector":
            n.setdefault("data", {})["code"] = CODE_BUILD_TURN_FOR_VECTOR
        elif nid == "cache-update-summary":
            d = n.setdefault("data", {})
            d["value"] = "memory"
        elif nid == "transform-for-vector-upsert":
            m = n.setdefault("data", {}).setdefault("mapping", {})
            m["metadata"] = "{{left.metadata}}"


def main() -> int:
    r = requests.post(
        f"{BASE}/api/v1/auth/login",
        data={"username": USER, "password": PWD},
        headers={"Content-Type": "application/x-www-form-urlencoded"},
        timeout=15,
    )
    if r.status_code != 200:
        print("登录失败:", r.status_code, r.text[:500], file=sys.stderr)
        return 1
    token = r.json().get("access_token")
    if not token:
        print("无 access_token", file=sys.stderr)
        return 1
    h = {"Authorization": f"Bearer {token}", "Content-Type": "application/json"}

    dup = requests.post(
        f"{BASE}/api/v1/agents/{SOURCE_AGENT_ID}/duplicate",
        headers=h,
        json={"name": NEW_NAME},
        timeout=30,
    )
    if dup.status_code != 201:
        print("复制失败:", dup.status_code, dup.text[:800], file=sys.stderr)
        return 1
    new_id = dup.json()["id"]
    print("已创建副本:", new_id, NEW_NAME)

    g = requests.get(f"{BASE}/api/v1/agents/{new_id}", headers=h, timeout=30)
    if g.status_code != 200:
        print("读取 Agent 失败:", g.text, file=sys.stderr)
        return 1
    agent = g.json()
    wf = agent["workflow_config"]
    _patch_nodes(wf)

    up = requests.put(
        f"{BASE}/api/v1/agents/{new_id}",
        headers=h,
        json={"description": NEW_DESC, "workflow_config": wf},
        timeout=60,
    )
    if up.status_code != 200:
        print("更新失败:", up.status_code, up.text[:800], file=sys.stderr)
        return 1
    print("已更新：LLM 提示、code-build-context / memory-value / vector-turn、cache-update-summary.value、upsert.metadata")
    print("Agent ID:", new_id)
    print(json.dumps({"id": new_id, "name": NEW_NAME}, ensure_ascii=False))
    return 0


if __name__ == "__main__":
    raise SystemExit(main())