playchat/backend/app/db/models.py

from datetime import datetime, timezone
from sqlalchemy import String, Text, DateTime, ForeignKey, Integer, Float
from sqlalchemy.orm import Mapped, mapped_column, relationship
from app.db.database import Base


class Session(Base):
    __tablename__ = "sessions"

    id: Mapped[str] = mapped_column(String, primary_key=True)
    title: Mapped[str] = mapped_column(String, default="New Chat")
    model: Mapped[str] = mapped_column(String, default="")
    created_at: Mapped[datetime] = mapped_column(DateTime, default=lambda: datetime.now(timezone.utc))
    updated_at: Mapped[datetime] = mapped_column(DateTime, default=lambda: datetime.now(timezone.utc), onupdate=lambda: datetime.now(timezone.utc))

    messages: Mapped[list["Message"]] = relationship(back_populates="session", cascade="all, delete-orphan", order_by="Message.created_at")
    memories: Mapped[list["Memory"]] = relationship(back_populates="session", cascade="all, delete-orphan")


class Message(Base):
    __tablename__ = "messages"

    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
    session_id: Mapped[str] = mapped_column(ForeignKey("sessions.id", ondelete="CASCADE"))
    role: Mapped[str] = mapped_column(String)  # "user" | "assistant" | "system"
    content: Mapped[str] = mapped_column(Text)
    created_at: Mapped[datetime] = mapped_column(DateTime, default=lambda: datetime.now(timezone.utc))

    session: Mapped["Session"] = relationship(back_populates="messages")
    tags: Mapped[list["MessageTag"]] = relationship(back_populates="message", cascade="all, delete-orphan")


class MessageTag(Base):
    __tablename__ = "message_tags"

    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
    message_id: Mapped[int] = mapped_column(ForeignKey("messages.id", ondelete="CASCADE"))
    tag: Mapped[str] = mapped_column(String)
    created_at: Mapped[datetime] = mapped_column(DateTime, default=lambda: datetime.now(timezone.utc))

    message: Mapped["Message"] = relationship(back_populates="tags")


class Memory(Base):
    __tablename__ = "memories"

    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
    session_id: Mapped[str] = mapped_column(ForeignKey("sessions.id", ondelete="CASCADE"))
    summary: Mapped[str] = mapped_column(Text)
    embedding_json: Mapped[str | None] = mapped_column(Text, nullable=True)  # 将来のベクトル検索用
    created_at: Mapped[datetime] = mapped_column(DateTime, default=lambda: datetime.now(timezone.utc))

    session: Mapped["Session"] = relationship(back_populates="memories")
    tags: Mapped[list["MemoryTag"]] = relationship(back_populates="memory", cascade="all, delete-orphan")


class MemoryTag(Base):
    __tablename__ = "memory_tags"

    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
    memory_id: Mapped[int] = mapped_column(ForeignKey("memories.id", ondelete="CASCADE"))
    tag: Mapped[str] = mapped_column(String)
    created_at: Mapped[datetime] = mapped_column(DateTime, default=lambda: datetime.now(timezone.utc))

    memory: Mapped["Memory"] = relationship(back_populates="tags")


class KnowledgeEntry(Base):
    """Web検索で得た知識。セッションを横断して蓄積し、以後の会話のシステムプロンプトに注入する。"""
    __tablename__ = "knowledge_entries"

    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
    query: Mapped[str] = mapped_column(String)
    summary: Mapped[str] = mapped_column(Text)
    created_at: Mapped[datetime] = mapped_column(DateTime, default=lambda: datetime.now(timezone.utc))

    sources: Mapped[list["KnowledgeSource"]] = relationship(back_populates="entry", cascade="all, delete-orphan")


class KnowledgeSource(Base):
    __tablename__ = "knowledge_sources"

    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
    entry_id: Mapped[int] = mapped_column(ForeignKey("knowledge_entries.id", ondelete="CASCADE"))
    title: Mapped[str] = mapped_column(String)
    url: Mapped[str] = mapped_column(String)

    entry: Mapped["KnowledgeEntry"] = relationship(back_populates="sources")


class LocalKnowledgeDoc(Base):
    """backend/knowledge_base/ 配下のMarkdownファイルとその埋め込みベクトル。

    話題に応じて関連ファイルをシステムプロンプトに注入するために使う。
    ファイル内容のハッシュが変わった時だけ埋め込みを再計算する。
    """
    __tablename__ = "local_knowledge_docs"

    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
    path: Mapped[str] = mapped_column(String, unique=True)
    title: Mapped[str] = mapped_column(String)
    content_hash: Mapped[str] = mapped_column(String)
    embedding_json: Mapped[str] = mapped_column(Text)
    updated_at: Mapped[datetime] = mapped_column(DateTime, default=lambda: datetime.now(timezone.utc), onupdate=lambda: datetime.now(timezone.utc))


class ApiLog(Base):
    __tablename__ = "api_logs"

    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
    session_id: Mapped[str | None] = mapped_column(ForeignKey("sessions.id", ondelete="SET NULL"), nullable=True)
    model: Mapped[str] = mapped_column(String)
    request_json: Mapped[str] = mapped_column(Text)   # JSON: messages array
    response_text: Mapped[str | None] = mapped_column(Text, nullable=True)
    error: Mapped[str | None] = mapped_column(Text, nullable=True)
    latency_ms: Mapped[float | None] = mapped_column(Float, nullable=True)
    created_at: Mapped[datetime] = mapped_column(DateTime, default=lambda: datetime.now(timezone.utc))