colcaroliandClaude Sonnet 4.6
4a08ba29fa
- Add CPU/GPU memory monitoring endpoint and frontend widget - Add Amazon Bedrock model support (API key / inference profiles) plus model selector entries - Add Discord weather/laundry notification job (JMA forecast + LLM-generated message) - Add Discord bot that replies to mentions via the configured LLM, with weather-aware context - Add scheduled "useless tips" job with separate webhook and quiet-hours skip - Switch FastAPI startup to lifespan handler, log viewer, markdown rendering, and toast UI additions Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
55 lines
2.7 KiB
Python
55 lines
2.7 KiB
Python
from datetime import datetime, timezone
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from sqlalchemy import String, Text, DateTime, ForeignKey, Integer, Float
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from sqlalchemy.orm import Mapped, mapped_column, relationship
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from app.db.database import Base
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class Session(Base):
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__tablename__ = "sessions"
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id: Mapped[str] = mapped_column(String, primary_key=True)
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title: Mapped[str] = mapped_column(String, default="New Chat")
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model: Mapped[str] = mapped_column(String, default="")
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created_at: Mapped[datetime] = mapped_column(DateTime, default=lambda: datetime.now(timezone.utc))
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updated_at: Mapped[datetime] = mapped_column(DateTime, default=lambda: datetime.now(timezone.utc), onupdate=lambda: datetime.now(timezone.utc))
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messages: Mapped[list["Message"]] = relationship(back_populates="session", cascade="all, delete-orphan", order_by="Message.created_at")
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memories: Mapped[list["Memory"]] = relationship(back_populates="session", cascade="all, delete-orphan")
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class Message(Base):
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__tablename__ = "messages"
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id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
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session_id: Mapped[str] = mapped_column(ForeignKey("sessions.id", ondelete="CASCADE"))
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role: Mapped[str] = mapped_column(String) # "user" | "assistant" | "system"
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content: Mapped[str] = mapped_column(Text)
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created_at: Mapped[datetime] = mapped_column(DateTime, default=lambda: datetime.now(timezone.utc))
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session: Mapped["Session"] = relationship(back_populates="messages")
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class Memory(Base):
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__tablename__ = "memories"
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id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
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session_id: Mapped[str] = mapped_column(ForeignKey("sessions.id", ondelete="CASCADE"))
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summary: Mapped[str] = mapped_column(Text)
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embedding_json: Mapped[str | None] = mapped_column(Text, nullable=True) # 将来のベクトル検索用
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created_at: Mapped[datetime] = mapped_column(DateTime, default=lambda: datetime.now(timezone.utc))
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session: Mapped["Session"] = relationship(back_populates="memories")
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class ApiLog(Base):
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__tablename__ = "api_logs"
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id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
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session_id: Mapped[str | None] = mapped_column(ForeignKey("sessions.id", ondelete="SET NULL"), nullable=True)
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model: Mapped[str] = mapped_column(String)
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request_json: Mapped[str] = mapped_column(Text) # JSON: messages array
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response_text: Mapped[str | None] = mapped_column(Text, nullable=True)
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error: Mapped[str | None] = mapped_column(Text, nullable=True)
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latency_ms: Mapped[float | None] = mapped_column(Float, nullable=True)
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created_at: Mapped[datetime] = mapped_column(DateTime, default=lambda: datetime.now(timezone.utc))
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