"""把 knowledge/ 下的文档切成可检索知识块，输出 JSONL（临时脚本，跑完即删）。

切片粒度决定检索质量。这里采用**叶子标题**策略：一个标题若其后没有更深的标题，
它就是一个切分点。相比"按固定级别切"，这个策略同时满足了三种真实情况：

- 有的条款带 `#### X.Y` 子条款（如反洗钱第九条 9.1–9.4）→ 按子条款切，粒度更细；
- 有的条款没有子条款（如反洗钱第十一条）→ 自己就是叶子，单独成块，不会被并进上一块；
- 有的章没有小节（如企业信息「一、公司基本信息」）→ 章本身就是叶子，内容不会丢。

标题路径保留完整上级链；若切分点本身不是「第X条」（例如反洗钱第十三条下的
`### 第一类：资金流转异常`），会把最近的条款名补进路径，避免块失去归属。
纯「目录」块直接丢弃。
"""

import json
import re
from pathlib import Path

HEADING = re.compile(r"^(#{1,6})\s+(.+?)\s*$")
CLAUSE = re.compile(r"^第[一二三四五六七八九十百]+条")

# 每个文件的入库配置：集合、编号前缀、可见性、版本与生效日期（取自文档头部）
SOURCES: dict[str, dict[str, str]] = {
    "policy/个人投资者适当性管理指南.md": {
        "collection": "fin_policy_collection", "prefix": "POL-AST", "visibility": "public",
        "version": "V3.2", "effective_date": "2024-01-15", "doc_no": "JR-AST-2024-001",
        "tags": "适当性,C1-C5,R1-R5,双录,冷静期",
    },
    # 反洗钱合规操作手册**不入客服知识库**：本业务只做公募基金，不涉及银行转账与资金划付，
    # 反洗钱属后续风控模块职责；且该手册标注「内部机密」、第十六条禁止向客户透露可疑交易
    # 信息，混入面向客户的知识库存在制度性冲突。
    "policy/理财产品销售管理办法.md": {
        "collection": "fin_policy_collection", "prefix": "POL-SPM", "visibility": "public",
        "version": "V3.0", "effective_date": "2024-02-01", "doc_no": "JR-SPM-2024-003",
        "tags": "理财产品销售,双录,冷静期,费率,投诉",
    },
    "product/个人理财产品手册.md": {
        "collection": "fin_product_collection", "prefix": "PROD", "visibility": "public",
        "version": "V2.8", "effective_date": "", "doc_no": "",
        "tags": "基金,银行理财,保险,费率,申赎",
    },
    # 只取「客户分层标准」与「各层级专属权益」两章：家族信托、资产配置流程、客户经理
    # 考核指标、隐私应急预案属内部管理内容，客户咨询用不到，不入库。
    "product/高净值客户服务规范.md": {
        "collection": "fin_product_collection", "prefix": "HNW", "visibility": "public",
        "version": "V2.1", "effective_date": "", "doc_no": "",
        "tags": "高净值,VIP分级,层级权益,费率优惠",
        "allow_chapters": ["一、", "二、"],
    },
    "company/企业信息.md": {
        "collection": "fin_faq_collection", "prefix": "COMP", "visibility": "public",
        "version": "", "effective_date": "", "doc_no": "",
        "tags": "公司信息,金融牌照,资质",
    },
}


def leaf_split_points(marks: list[tuple[int, int, str]]) -> set[int]:
    """叶子标题的行号集合：其后没有更深标题的标题。"""
    points: set[int] = set()
    for position, (index, level, _title) in enumerate(marks):
        following = marks[position + 1] if position + 1 < len(marks) else None
        if following is not None and following[1] > level:
            continue  # 有子标题，不是叶子
        points.add(index)
    return points


def chunk_markdown(text: str) -> list[dict[str, str]]:
    lines = text.splitlines()
    marks: list[tuple[int, int, str]] = []
    for index, line in enumerate(lines):
        match = HEADING.match(line)
        if match:
            marks.append((index, len(match.group(1)), match.group(2).strip()))
    marks_by_line = {index: (level, title) for index, level, title in marks}
    split_points = leaf_split_points(marks)

    stack: dict[int, str] = {}
    chunks: list[dict[str, str]] = []
    buffer: list[str] = []
    meta: dict[str, str] | None = None
    last_clause = ""

    def has_body() -> bool:
        return any(not HEADING.match(line) and line.strip() for line in buffer)

    def flush() -> None:
        nonlocal buffer, meta
        if meta is not None and has_body():
            body = "\n".join(buffer).strip()
            if body:
                chunks.append({**meta, "content": body})
        buffer = []

    for index, line in enumerate(lines):
        mark = marks_by_line.get(index)
        if mark is not None:
            level, title = mark
            stack[level] = title
            for deeper in [key for key in stack if key > level]:
                del stack[deeper]
            if CLAUSE.match(title):
                last_clause = title
            if index in split_points:
                flush()
                # 所属章取「最近的上级标题」，而不是最外层文档标题（sorted 后取首个会拿到 h1）
                ancestors = [key for key in stack if key < level]
                chapter = stack[max(ancestors)] if ancestors else ""
                path = [stack[key] for key in sorted(stack) if key <= level]
                if not CLAUSE.match(title) and last_clause and last_clause not in path:
                    path = [item for item in (chapter, last_clause) if item] + [title]
                meta = {"title": " · ".join(path), "chapter": chapter, "section": title}
        buffer.append(line)
    flush()
    return [chunk for chunk in chunks if chunk["section"].strip() != "目录"]


def chunk_qa(text: str) -> list[dict[str, str]]:
    chunks: list[dict[str, str]] = []
    for line in text.splitlines():
        line = line.strip()
        if not line or "\t" not in line:
            continue
        question, _, answer = line.partition("\t")
        chunks.append({
            "title": question.strip(),
            "chapter": "高频问答",
            "section": question.strip(),
            "content": f"问：{question.strip()}\n答：{answer.strip()}",
        })
    return chunks


TABLE_ROW = re.compile(r"^\|(.+)\|\s*$")
LEADING_NUMBER = re.compile(r"^\d+(?:\.\d+)*\s*")


def expand_table_rows(chunk: dict[str, str], parent_id: str) -> list[dict[str, object]]:
    """把 Markdown 表格的每一行拆成自解释的小块（父块照旧保留）。

    为什么需要：现在的粒度是"一个叶子标题 = 一块"，产品手册里就是**整个产品小节**
    （表格 + 说明）成一块。于是客户问「起投多少」和问「风险高吗」命中同一块、拿到
    **完全相同**的整节内容——客户会觉得客服没听懂问题，只是把说明书重贴一遍。
    顺带地，整节几百字的向量是"整节的混合语义"，与"起投多少"这种具体小问题相似度
    天然偏低（实测该问句向量 top1 只有 0.6291，够不到 0.75 门槛）。

    小块必须**自解释**：只回「1万元」客户不知道说的是哪个产品，所以带上产品名与行标签。
    父块保留，客户问「这个产品怎么样」时仍要能拿到完整一节。
    """
    blocks: list[dict[str, object]] = []
    header: list[str] = []
    name = LEADING_NUMBER.sub("", chunk["section"]).strip() or chunk["section"]
    for line in chunk["content"].splitlines():
        match = TABLE_ROW.match(line.strip())
        if not match:
            continue
        cells = [cell.strip() for cell in match.group(1).split("|")]
        if all(set(cell) <= {"-", ":", " "} for cell in cells):
            continue  # 分隔行 |---|
        if not header:
            header = cells
            continue
        if len(cells) < 2:
            continue
        label, value = cells[0], cells[1]
        if not label or not value:
            continue
        blocks.append({
            "title": f"{chunk['title']} · {label}",
            "chapter": chunk["chapter"],
            "section": f"{name} · {label}",
            "content": f"{name}：{label} {value}",
            "parent_id": parent_id,
        })
    return blocks


records: list[dict[str, object]] = []
for relative, config in SOURCES.items():
    text = (Path("knowledge") / relative).read_text(encoding="utf-8")
    chunks = chunk_markdown(text)
    # 章节白名单：只保留指定章下的块（用于剔除内部管理章节，如高净值规范只留分级与权益）
    allowed = config.get("allow_chapters")
    if isinstance(allowed, list):
        chunks = [
            chunk for chunk in chunks
            if any(chunk["chapter"].startswith(prefix) for prefix in allowed)
        ]
    for order, chunk in enumerate(chunks, 1):
        parent_id = f"{config['prefix']}-{order:03d}"
        records.append({
            "doc_id": parent_id,
            "collection": config["collection"],
            "title": chunk["title"],
            "content": chunk["content"],
            "chapter": chunk["chapter"],
            "section": chunk["section"],
            "tags": config["tags"],
            "doc_no": config["doc_no"],
            "version": config["version"],
            "effective_date": config["effective_date"],
            "expire_date": "", "source_url": "", "reviewer": "",
            "source_file": relative,
            "visibility": config["visibility"],
            "chars": len(chunk["content"]),
        })
        # 行级子块：挂在父块 doc_id 下（PROD-007-01 这种），父块编号不受新增子块影响，
        # 因此反复重跑本脚本得到的 doc_id 是稳定的。
        for row_order, block in enumerate(expand_table_rows(chunk, parent_id), 1):
            records.append({
                "doc_id": f"{parent_id}-{row_order:02d}",
                "collection": config["collection"],
                "title": block["title"],
                "content": block["content"],
                "chapter": block["chapter"],
                "section": block["section"],
                "tags": config["tags"],
                "doc_no": config["doc_no"],
                "version": config["version"],
                "effective_date": config["effective_date"],
                "expire_date": "", "source_url": "", "reviewer": "",
                "source_file": relative,
                "visibility": config["visibility"],
                "chars": len(str(block["content"])),
            })

for order, chunk in enumerate(
    chunk_qa((Path("knowledge") / "faq/高频问答对.txt").read_text(encoding="utf-8")), 1
):
    records.append({
        "doc_id": f"FAQ-{order:04d}",
        "collection": "fin_faq_collection",
        "title": chunk["title"], "content": chunk["content"],
        "chapter": chunk["chapter"], "section": chunk["section"],
        "tags": "高频问答,FAQ",
        "doc_no": "", "version": "", "effective_date": "", "expire_date": "",
        "source_url": "", "reviewer": "",
        "source_file": "faq/高频问答对.txt", "visibility": "public",
        "chars": len(chunk["content"]),
    })

(Path("knowledge") / "_chunks.jsonl").write_text(
    "\n".join(json.dumps(record, ensure_ascii=False) for record in records), encoding="utf-8"
)

lines: list[str] = [f"总块数：{len(records)}\n"]
by_collection: dict[str, list[dict[str, object]]] = {}
for record in records:
    by_collection.setdefault(str(record["collection"]), []).append(record)
for name, group in sorted(by_collection.items()):
    sizes = sorted(int(record["chars"]) for record in group)
    lines.append(
        f"{name}: {len(group)} 块，字符数 最小 {sizes[0]} / 中位 {sizes[len(sizes)//2]} / 最大 {sizes[-1]}"
    )

lines.append("\n各文件块数：")
by_file: dict[str, int] = {}
for record in records:
    by_file[str(record["source_file"])] = by_file.get(str(record["source_file"]), 0) + 1
for name, count in sorted(by_file.items()):
    lines.append(f"  {name}: {count}")

over = [record for record in records if int(record["chars"]) > 1200]
lines.append(f"\n超过 1200 字符的块：{len(over)} 个")
for record in over[:10]:
    lines.append(f"  {record['doc_id']}  {record['chars']} 字符  {str(record['title'])[:64]}")

lines.append("\n反洗钱手册的块标题（核对「第一类」归属是否带上了第十三条）：")
for record in records:
    if str(record["doc_id"]).startswith("POL-AML"):
        lines.append(f"  {record['doc_id']}  {int(record['chars']):>5} 字符  {str(record['title'])[:70]}")

Path("_chunks_report.txt").write_text("\n".join(lines), encoding="utf-8")
print(f"已生成 {len(records)} 块 → knowledge/_chunks.jsonl；报告见 _chunks_report.txt")