#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""作业 2 · 客服意图路由工作流 graph 定义（给草稿同步用）。

结构：用户输入 → LLM 意图识别 → 代码抽出类别 → IF/ELSE
  ├ 知识库问答 → 知识库检索 → 生成回答 → 结束
  ├ 工单报修 → HTTP 工单 API → 返回工单号 → 结束
  ├ 人工客服 → 转人工提示 → 结束
  └ 其他 → 兜底回复 → 结束

不用 question-classifier：Qwen3.6 带 reasoning 时经常不吐 ```json```，
该节点会报 could not find json block。
"""
from __future__ import annotations

from typing import Any

import _config as C

CLS_KB = "cls_kb"
CLS_TICKET = "cls_ticket"
CLS_HUMAN = "cls_human"
CLS_OTHER = "cls_other"

N_START = "start"
N_LLM_INTENT = "llm_intent"
N_PARSE = "parse_intent"
N_IF = "route"
N_KR = "kr"
N_LLM_KB = "llm_kb"
N_HTTP = "http_ticket"
N_TPL_TICKET = "tpl_ticket"
N_TPL_HUMAN = "tpl_human"
N_TPL_OTHER = "tpl_other"
N_END_KB = "end_kb"
N_END_TICKET = "end_ticket"
N_END_HUMAN = "end_human"
N_END_OTHER = "end_other"

PARSE_CODE = r'''
import re

LABELS = ("人工客服", "工单报修", "知识库问答", "其他")


def main(text: str) -> dict:
    raw = text or ""
    cleaned = re.sub(r"<think[^>]*>[\s\S]*?</think>", "", raw, flags=re.I)
    # 优先看清洗后的正文，避免思考链里同时出现多个类别名
    for blob in (cleaned, raw):
        for lab in LABELS:
            if lab in blob:
                return {"intent": lab}
    low = (cleaned or raw).lower()
    if any(k in low for k in ("投诉", "complain", "human", "转人工")):
        return {"intent": "人工客服"}
    if any(k in low for k in ("报修", "repair", "ticket", "工单")):
        return {"intent": "工单报修"}
    if any(k in low for k in ("请假", "制度", "knowledge", "知识库")):
        return {"intent": "知识库问答"}
    return {"intent": "其他"}
'''.lstrip()

INTENT_SYS = """你是客服意图分类器。根据用户原话，只输出下面四个标签之一，不要解释、不要思考过程、不要 Markdown。

标签：
- 人工客服：投诉、举报、找领导、要人工、转人工。例句：我要投诉
- 工单报修：报修、设备故障、开维修单。例句：帮我报修设备
- 知识库问答：制度、流程、请假、加班、打卡、福利。例句：怎么请假
- 其他：闲聊或无法归类

只输出这四个中文词之一。"""


def _model(max_tokens: int, temperature: float = 0.2) -> dict[str, Any]:
    return {
        "provider": C.OPENAI_PROVIDER,
        "name": C.QWEN_MODEL,
        "mode": "chat",
        "completion_params": {
            "temperature": temperature,
            "max_tokens": max_tokens,
            "enable_thinking": False,
        },
    }


def _node(nid: str, ntype: str, title: str, data: dict[str, Any], x: float, y: float, h: int = 110) -> dict[str, Any]:
    payload = {
        "type": ntype,
        "title": title,
        "desc": "",
        "selected": False,
        **data,
    }
    return {
        "id": nid,
        "type": "custom",
        "position": {"x": x, "y": y},
        "positionAbsolute": {"x": x, "y": y},
        "sourcePosition": "right",
        "targetPosition": "left",
        "width": 244,
        "height": h,
        "data": payload,
    }


def _edge(src: str, dst: str, src_type: str, dst_type: str, handle: str = "source") -> dict[str, Any]:
    return {
        "id": "%s-%s-%s" % (src, handle, dst),
        "source": src,
        "sourceHandle": handle,
        "target": dst,
        "targetHandle": "target",
        "type": "custom",
        "zIndex": 0,
        "data": {
            "sourceType": src_type,
            "targetType": dst_type,
            "isInIteration": False,
        },
    }


FEATURES: dict[str, Any] = {
    "file_upload": {
        "image": {
            "enabled": False,
            "number_limits": 3,
            "transfer_methods": ["local_file", "remote_url"],
        }
    },
    "opening_statement": "",
    "retriever_resource": {"enabled": True},
    "sensitive_word_avoidance": {"enabled": False},
    "speech_to_text": {"enabled": False},
    "suggested_questions": [],
    "suggested_questions_after_answer": {"enabled": False},
    "text_to_speech": {"enabled": False, "language": "", "voice": ""},
}


def build_graph() -> dict[str, Any]:
    ticket_url = C.TICKET_API_BASE.rstrip("/") + "/ticket"
    body_json = '{"query": "{{#%s.query#}}", "category": "repair"}' % N_START
    nodes = [
        _node(
            N_START,
            "start",
            "用户输入",
            {
                "variables": [
                    {
                        "variable": "query",
                        "label": "用户问题",
                        "type": "paragraph",
                        "required": True,
                        "max_length": 2000,
                        "options": [],
                    }
                ]
            },
            40,
            420,
            90,
        ),
        _node(
            N_LLM_INTENT,
            "llm",
            "意图识别",
            {
                "model": _model(1024, 0.0),
                "prompt_template": [
                    {"role": "system", "text": INTENT_SYS, "edition_type": "basic"},
                    {
                        "role": "user",
                        "text": "用户原话：{{#%s.query#}}" % N_START,
                        "edition_type": "basic",
                    },
                ],
                "context": {"enabled": False, "variable_selector": []},
                "vision": {"enabled": False},
            },
            330,
            400,
            140,
        ),
        _node(
            N_PARSE,
            "code",
            "抽出意图标签",
            {
                "variables": [
                    {"variable": "text", "value_selector": [N_LLM_INTENT, "text"]}
                ],
                "code_language": "python3",
                "code": PARSE_CODE,
                "outputs": {"intent": {"type": "string", "children": None}},
            },
            620,
            400,
            100,
        ),
        _node(
            N_IF,
            "if-else",
            "意图路由",
            {
                "cases": [
                    {
                        "case_id": CLS_KB,
                        "logical_operator": "and",
                        "conditions": [
                            {
                                "id": "c_kb",
                                "variable_selector": [N_PARSE, "intent"],
                                "comparison_operator": "is",
                                "value": "知识库问答",
                            }
                        ],
                    },
                    {
                        "case_id": CLS_TICKET,
                        "logical_operator": "and",
                        "conditions": [
                            {
                                "id": "c_ticket",
                                "variable_selector": [N_PARSE, "intent"],
                                "comparison_operator": "is",
                                "value": "工单报修",
                            }
                        ],
                    },
                    {
                        "case_id": CLS_HUMAN,
                        "logical_operator": "and",
                        "conditions": [
                            {
                                "id": "c_human",
                                "variable_selector": [N_PARSE, "intent"],
                                "comparison_operator": "is",
                                "value": "人工客服",
                            }
                        ],
                    },
                    {
                        "case_id": CLS_OTHER,
                        "logical_operator": "and",
                        "conditions": [
                            {
                                "id": "c_other",
                                "variable_selector": [N_PARSE, "intent"],
                                "comparison_operator": "is",
                                "value": "其他",
                            }
                        ],
                    },
                ]
            },
            900,
            380,
            220,
        ),
        _node(
            N_KR,
            "knowledge-retrieval",
            "知识库检索",
            {
                "query_variable_selector": [N_START, "query"],
                "dataset_ids": [C.DS_ID],
                "retrieval_mode": "multiple",
                "multiple_retrieval_config": {
                    "top_k": 5,
                    "score_threshold": None,
                    "reranking_enable": True,
                    "reranking_mode": "reranking_model",
                    "reranking_model": {
                        "provider": C.XINFERENCE_PROVIDER,
                        "model": C.RERANK_MODEL,
                    },
                },
            },
            1180,
            40,
            100,
        ),
        _node(
            N_LLM_KB,
            "llm",
            "生成回答",
            {
                "model": _model(2048, 0.3),
                "prompt_template": [
                    {
                        "role": "system",
                        "text": (
                            "你是播城内部行政客服。只根据知识库片段回答，不要编造。"
                            "若片段不足，明确说不知道并建议转人工。\n"
                            "使用 <context></context> 中的资料：\n"
                            "<context>\n{{#context#}}\n</context>"
                        ),
                        "edition_type": "basic",
                    },
                    {
                        "role": "user",
                        "text": "用户问题：{{#%s.query#}}" % N_START,
                        "edition_type": "basic",
                    },
                ],
                "context": {
                    "enabled": True,
                    "variable_selector": [N_KR, "result"],
                },
                "vision": {"enabled": False},
            },
            1480,
            40,
            150,
        ),
        _node(
            N_END_KB,
            "end",
            "结束·知识库",
            {
                "outputs": [
                    {"variable": "answer", "value_selector": [N_LLM_KB, "text"]}
                ]
            },
            1780,
            40,
            90,
        ),
        _node(
            N_HTTP,
            "http-request",
            "调用工单API",
            {
                "method": "post",
                "url": ticket_url,
                "authorization": {"type": "no-auth", "config": None},
                "headers": "Content-Type:application/json",
                "params": "",
                "body": {
                    "type": "json",
                    "data": [{"type": "text", "key": "", "value": body_json}],
                },
                "timeout": {
                    "connect": 10,
                    "read": 30,
                    "write": 30,
                    "max_connect_timeout": 10,
                    "max_read_timeout": 60,
                    "max_write_timeout": 60,
                },
                "ssl_verify": False,
            },
            1180,
            280,
            150,
        ),
        _node(
            N_TPL_TICKET,
            "template-transform",
            "返回工单号",
            {
                "template": "已为您创建报修工单。\n{{ body }}\n请保存工单号，维修人员会联系您。",
                "variables": [
                    {"variable": "body", "value_selector": [N_HTTP, "body"]}
                ],
            },
            1480,
            280,
            90,
        ),
        _node(
            N_END_TICKET,
            "end",
            "结束·工单",
            {
                "outputs": [
                    {"variable": "answer", "value_selector": [N_TPL_TICKET, "output"]}
                ]
            },
            1780,
            280,
            90,
        ),
        _node(
            N_TPL_HUMAN,
            "template-transform",
            "转人工提示",
            {
                "template": (
                    "正在为您转接人工客服。投诉类诉求需要人工跟进，"
                    "请保持在线，工作人员会尽快接入。"
                ),
                "variables": [],
            },
            1180,
            500,
            90,
        ),
        _node(
            N_END_HUMAN,
            "end",
            "结束·人工",
            {
                "outputs": [
                    {"variable": "answer", "value_selector": [N_TPL_HUMAN, "output"]}
                ]
            },
            1480,
            500,
            90,
        ),
        _node(
            N_TPL_OTHER,
            "template-transform",
            "兜底回复",
            {
                "template": (
                    "我暂时无法判断您的具体诉求。您可以：\n"
                    "1）问制度流程（例如怎么请假）；\n"
                    "2）报修设备；\n"
                    "3）明确说要投诉或转人工。"
                ),
                "variables": [],
            },
            1180,
            680,
            90,
        ),
        _node(
            N_END_OTHER,
            "end",
            "结束·其他",
            {
                "outputs": [
                    {"variable": "answer", "value_selector": [N_TPL_OTHER, "output"]}
                ]
            },
            1480,
            680,
            90,
        ),
    ]
    edges = [
        _edge(N_START, N_LLM_INTENT, "start", "llm"),
        _edge(N_LLM_INTENT, N_PARSE, "llm", "code"),
        _edge(N_PARSE, N_IF, "code", "if-else"),
        _edge(N_IF, N_KR, "if-else", "knowledge-retrieval", CLS_KB),
        _edge(N_KR, N_LLM_KB, "knowledge-retrieval", "llm"),
        _edge(N_LLM_KB, N_END_KB, "llm", "end"),
        _edge(N_IF, N_HTTP, "if-else", "http-request", CLS_TICKET),
        _edge(N_HTTP, N_TPL_TICKET, "http-request", "template-transform"),
        _edge(N_TPL_TICKET, N_END_TICKET, "template-transform", "end"),
        _edge(N_IF, N_TPL_HUMAN, "if-else", "template-transform", CLS_HUMAN),
        _edge(N_TPL_HUMAN, N_END_HUMAN, "template-transform", "end"),
        _edge(N_IF, N_TPL_OTHER, "if-else", "template-transform", CLS_OTHER),
        _edge(N_TPL_OTHER, N_END_OTHER, "template-transform", "end"),
        _edge(N_IF, N_TPL_OTHER, "if-else", "template-transform", "false"),
    ]
    return {
        "nodes": nodes,
        "edges": edges,
        "viewport": {"x": 0, "y": 0, "zoom": 0.65},
    }