"""知识域模型（M3）：knowledge_doc。

三条设计约束（对应 PRODUCT-PLAN 的 D1/D3）：

1. **知识也是 `item`**。标题、正文、标签、评论、事件流全部复用；`knowledge_doc`
   只补"从哪来的、是什么类型、什么时候被人认可过"。同一套列表、搜索、详情、权限对知识依然成立。
2. **知识必须有来源**（D3）。`source_kind` 为 `task` / `ticket` 时必须给出 `source_item_id`，
   只有 `manual`（自己记的笔记）允许留空。这条写在数据库 CHECK 里，不靠 API 自律——
   将来接 LLM 自动归档时，没有来源的知识等于"没人能核对的断言"，比没有更糟。
3. **状态机是 `draft → in_review → published → archived`**。草稿可以随时改，发布的要有
   人来点头（`published_at` 记时间），归档的不删只藏（历史结论不该消失，只该退出检索）。
"""

from __future__ import annotations

import uuid
from datetime import datetime

from sqlalchemy import (
    CheckConstraint,
    DateTime,
    Float,
    ForeignKey,
    Index,
    String,
    Text,
    UniqueConstraint,
)
from sqlalchemy.dialects.postgresql import ARRAY
from sqlalchemy.dialects.postgresql import UUID as PgUUID
from sqlalchemy.orm import Mapped, mapped_column

from .models import Base, Timestamps, new_uuid

# 知识状态机（与任务/工单共用 item.status 一列，取值集合不同）
KNOWLEDGE_STATUSES: tuple[str, ...] = ("draft", "in_review", "published", "archived")

DOC_TYPES: tuple[str, ...] = ("note", "howto", "faq", "postmortem", "decision")
SOURCE_KINDS: tuple[str, ...] = ("manual", "task", "ticket")
GENERATED_BY: tuple[str, ...] = ("human", "rule")


class KnowledgeDoc(Base, Timestamps):
    """知识的附加信息，与 item 一比一。"""

    __tablename__ = "knowledge_doc"
    __table_args__ = (
        Index("ix_knowledge_ws_status", "workspace_id", "status_hint"),
        Index("ix_knowledge_ws_source", "workspace_id", "source_item_id"),
        CheckConstraint(
            "source_kind = 'manual' OR source_item_id IS NOT NULL",
            name="ck_knowledge_source_required",
        ),
        CheckConstraint("chunk_count >= 0", name="ck_knowledge_chunk_count"),
    )

    # item_id 既是主键又是外键：一条 item 最多一条 knowledge_doc，天然 1:1
    item_id: Mapped[uuid.UUID] = mapped_column(
        PgUUID(as_uuid=True), ForeignKey("item.id", ondelete="CASCADE"), primary_key=True
    )
    workspace_id: Mapped[uuid.UUID] = mapped_column(
        PgUUID(as_uuid=True), ForeignKey("workspace.id", ondelete="CASCADE"), nullable=False
    )
    author_id: Mapped[uuid.UUID] = mapped_column(
        PgUUID(as_uuid=True), ForeignKey("app_user.id", ondelete="RESTRICT"), nullable=False
    )
    # 知识的来源。NULL 只允许出现在 source_kind='manual' 上（见 CHECK）
    source_item_id: Mapped[uuid.UUID | None] = mapped_column(
        PgUUID(as_uuid=True), ForeignKey("item.id", ondelete="SET NULL")
    )
    source_kind: Mapped[str] = mapped_column(String(16), nullable=False, default="manual")
    doc_type: Mapped[str] = mapped_column(String(16), nullable=False, default="note")
    # 冗余一份状态：列表页要按知识自己的口径筛，不想每次都回 item 表
    status_hint: Mapped[str] = mapped_column(String(16), nullable=False, default="draft")
    summary: Mapped[str] = mapped_column(Text, nullable=False, default="")
    generated_by: Mapped[str] = mapped_column(String(16), nullable=False, default="human")
    published_at: Mapped[datetime | None] = mapped_column(DateTime(timezone=True))
    archived_at: Mapped[datetime | None] = mapped_column(DateTime(timezone=True))
    chunk_count: Mapped[int] = mapped_column(nullable=False, default=0)


class Chunk(Base):
    """知识正文的切片。

    为什么切片与文档同库：检索要按工作区过滤，跨库做权限校验就得把两边数据捞到应用里，
    既不安全也不快。向量本身是普通数组，换 pgvector 时只换列类型与索引。
    """

    __tablename__ = "chunk"
    __table_args__ = (
        UniqueConstraint("item_id", "idx", name="uq_chunk_item_idx"),
        Index("ix_chunk_ws_item", "workspace_id", "item_id"),
        {"comment": "知识正文切片，检索的最小单位"},
    )

    id: Mapped[uuid.UUID] = mapped_column(PgUUID(as_uuid=True), primary_key=True, default=new_uuid)
    workspace_id: Mapped[uuid.UUID] = mapped_column(PgUUID(as_uuid=True), nullable=False)
    item_id: Mapped[uuid.UUID] = mapped_column(
        PgUUID(as_uuid=True), ForeignKey("item.id", ondelete="CASCADE"), nullable=False
    )
    # 片内顺序，用于把命中片段按原顺序还原上下文
    idx: Mapped[int] = mapped_column(nullable=False)
    heading: Mapped[str | None] = mapped_column(String(200))
    content: Mapped[str] = mapped_column(Text, nullable=False)
    char_count: Mapped[int] = mapped_column(nullable=False, default=0)
    # 向量。hash 提供方是 256 维，OpenAI 兼容端点通常是 1024/1536 维，
    # 所以不固定维度——换模型时老切片要么重算，要么维度不匹配（检索会拒绝并提示重建）。
    embedding: Mapped[list[float] | None] = mapped_column(ARRAY(Float))
    embed_model: Mapped[str | None] = mapped_column(String(64))


KNOWLEDGE_TENANT_TABLES: tuple[str, ...] = ("knowledge_doc", "chunk")