A 1536-dimension vector spelled out as JSON decimals is ~31 KB. The same
numbers packed as float32 are 6,144 bytes, and the whole bank is read on
every turn, so those bytes are paid over and over.
It is a format change, not a precision trade: the endpoints compute in
float32 and render that into JSON, so converting back recovers the original
bits exactly. Nothing is re-embedded and no API call is made -- migration 38
is a pure repack of what is already stored.
Unlike migrations 36 and 37 this backfill cannot be expressed in portable
SQL, so it comes through Python, batched, and pays a one-time read of every
vector to stop paying three megabytes a turn.
The JSON column stays, still written through set_vector, so a rollback finds
the vectors intact. Reading from the blob comes next; a follow-up migration
drops the old column once that is verified.
Migration SQL can now be a {dialect: sql} map -- BLOB and BYTEA have no
common spelling, and every Postgres deploy replays this one.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_015CYEJKobJ2Re4Dv7qUoSA7
393 lines
20 KiB
Python
393 lines
20 KiB
Python
from datetime import datetime, timezone
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from sqlalchemy import (
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JSON, Boolean, Column, DateTime, Float, ForeignKey, Integer, LargeBinary, String,
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Table, Text,
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)
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from sqlalchemy.orm import Mapped, mapped_column, relationship
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from .database import Base
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def utcnow() -> datetime:
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return datetime.now(timezone.utc)
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class User(Base):
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"""Phase 8 — optional accounts.
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Three kinds of rows share this table:
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- the "local user" (email NULL, is_guest False): auto-created in
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single-user/local mode; owns everything a pre-Phase-8 DB had;
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- guests (email NULL, is_guest True): created on first visit in
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multi-user mode, identified only by their session cookie;
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- registered users (email set): a guest upgraded in place, so their
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data survives registration with no re-parenting.
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"""
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__tablename__ = "users"
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id: Mapped[int] = mapped_column(primary_key=True)
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email: Mapped[str | None] = mapped_column(String(320), unique=True, nullable=True)
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password_hash: Mapped[str | None] = mapped_column(String(300), nullable=True)
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is_guest: Mapped[bool] = mapped_column(Boolean, default=True)
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created_at: Mapped[datetime] = mapped_column(DateTime, default=utcnow)
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last_seen_at: Mapped[datetime | None] = mapped_column(DateTime, nullable=True)
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# Shared demo key usage (resets when the UTC date changes).
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demo_turns_used: Mapped[int] = mapped_column(Integer, default=0)
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demo_turns_date: Mapped[str] = mapped_column(String(10), default="")
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scenario_scripts = Table(
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"scenario_scripts",
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Base.metadata,
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Column("scenario_id", ForeignKey("scenarios.id", ondelete="CASCADE"), primary_key=True),
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Column("script_id", ForeignKey("scripts.id", ondelete="CASCADE"), primary_key=True),
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)
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class Scenario(Base):
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__tablename__ = "scenarios"
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id: Mapped[int] = mapped_column(primary_key=True)
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# NULL owner + is_public = seeded demo content, readable by everyone.
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user_id: Mapped[int | None] = mapped_column(
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ForeignKey("users.id", ondelete="CASCADE"), nullable=True
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)
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is_public: Mapped[bool] = mapped_column(Boolean, default=False)
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title: Mapped[str] = mapped_column(String(200), default="Untitled Scenario")
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description: Mapped[str] = mapped_column(Text, default="")
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prompt: Mapped[str] = mapped_column(Text, default="")
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# Plot components (AI Dungeon terminology; `memory` == Plot Essentials)
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memory: Mapped[str] = mapped_column(Text, default="")
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authors_note: Mapped[str] = mapped_column(Text, default="")
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ai_instructions: Mapped[str] = mapped_column(Text, default="")
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tags: Mapped[str] = mapped_column(String(500), default="")
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# Cover art. Either an external "https://…" URL or an inline
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# "data:image/…;base64,…" URI (the editor downscales uploads before storing
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# one). Empty means the UI falls back to an emoji sigil or generated art.
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# Kept in the row rather than on disk because Render's free tier has no
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# persistent volume, and it makes export bundles self-contained.
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image: Mapped[str] = mapped_column(Text, default="")
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# A single emoji or glyph used when there's no `image` — cheap art for
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# scenarios nobody wants to find a picture for. Separate from `image`
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# because it's a character, not a locator: no fetch, no cache, no bytes.
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icon: Mapped[str] = mapped_column(String(16), default="")
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# Phase 12: RPG world-state template — stat definitions (bands, rules) and
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# milestones. NULL/empty means this scenario has no RPG layer.
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stat_schema: Mapped[dict | None] = mapped_column(JSON, nullable=True)
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created_at: Mapped[datetime] = mapped_column(DateTime, default=utcnow)
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updated_at: Mapped[datetime] = mapped_column(DateTime, default=utcnow, onupdate=utcnow)
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story_cards: Mapped[list["StoryCard"]] = relationship(
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back_populates="scenario", cascade="all, delete-orphan"
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)
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adventures: Mapped[list["Adventure"]] = relationship(back_populates="scenario")
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scripts: Mapped[list["Script"]] = relationship(secondary=scenario_scripts)
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class Adventure(Base):
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__tablename__ = "adventures"
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id: Mapped[int] = mapped_column(primary_key=True)
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user_id: Mapped[int | None] = mapped_column(
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ForeignKey("users.id", ondelete="CASCADE"), nullable=True
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)
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scenario_id: Mapped[int | None] = mapped_column(
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ForeignKey("scenarios.id", ondelete="SET NULL"), nullable=True
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)
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title: Mapped[str] = mapped_column(String(200), default="Untitled Adventure")
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memory: Mapped[str] = mapped_column(Text, default="")
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authors_note: Mapped[str] = mapped_column(Text, default="")
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ai_instructions: Mapped[str] = mapped_column(Text, default="")
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story_summary: Mapped[str] = mapped_column(Text, default="")
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script_state: Mapped[dict] = mapped_column(JSON, default=dict)
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# Phase 12: live RPG world state (world/player/npc stats + milestones),
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# instantiated from the scenario's stat_schema. Empty when there's no RPG layer.
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world_state: Mapped[dict] = mapped_column(JSON, default=dict)
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# The ${Placeholder} answers collected when this adventure was started, kept
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# so "Update from scenario" can re-fill freshly copied scenario text with the
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# same values. NULL for adventures created before this column existed.
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placeholders: Mapped[dict | None] = mapped_column(JSON, nullable=True)
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# Phase 6: opt-in per adventure (extra AI calls)
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auto_summarize: Mapped[bool] = mapped_column(Boolean, default=False)
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memory_bank_enabled: Mapped[bool] = mapped_column(Boolean, default=False)
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# How many actions have already been folded into memories / the story summary.
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memory_cursor: Mapped[int] = mapped_column(Integer, default=0)
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summary_cursor: Mapped[int] = mapped_column(Integer, default=0)
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created_at: Mapped[datetime] = mapped_column(DateTime, default=utcnow)
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updated_at: Mapped[datetime] = mapped_column(DateTime, default=utcnow, onupdate=utcnow)
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scenario: Mapped[Scenario | None] = relationship(back_populates="adventures")
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story_cards: Mapped[list["StoryCard"]] = relationship(
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back_populates="adventure", cascade="all, delete-orphan"
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)
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actions: Mapped[list["Action"]] = relationship(
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back_populates="adventure",
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cascade="all, delete-orphan",
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order_by="Action.index",
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)
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scripts: Mapped[list["AdventureScript"]] = relationship(
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back_populates="adventure",
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cascade="all, delete-orphan",
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order_by="AdventureScript.position",
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)
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memories: Mapped[list["Memory"]] = relationship(
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back_populates="adventure",
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cascade="all, delete-orphan",
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order_by="Memory.id",
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)
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class Memory(Base):
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"""Phase 6: an auto-summarized (or hand-written) fact about the adventure.
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The vector lives in `embedding_blob` as packed float32 (see vectors.py).
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NULL until embedded, which also marks it for backfill when an embedding
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model becomes available.
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Cosine ranking happens in Python, which means the vectors cross the wire.
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The original comment here sized that by count — "fine at a few hundred" —
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and it was wrong by the only measure that mattered: a few hundred JSON
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vectors is ten megabytes, fetched fresh every turn. Weigh new columns in
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bytes.
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"""
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__tablename__ = "memories"
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id: Mapped[int] = mapped_column(primary_key=True)
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adventure_id: Mapped[int] = mapped_column(ForeignKey("adventures.id", ondelete="CASCADE"))
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text: Mapped[str] = mapped_column(Text, default="")
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# Superseded by embedding_blob and written alongside it, so the two stay in
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# step until a follow-up migration drops this one. Still the column the
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# ranking path reads; that moves next.
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embedding: Mapped[list | None] = mapped_column(JSON, nullable=True)
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# The vector, little-endian float32. Deferred because it is wider than the
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# rest of the row put together and exactly one code path wants it: anything
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# bulk-loading memories (the Memories drawer, eviction, the embed queue)
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# must project the columns it needs rather than load whole entities.
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embedding_blob: Mapped[bytes | None] = mapped_column(
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LargeBinary, nullable=True, deferred=True
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)
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# Action index range this memory summarizes (null for manual memories).
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source_start: Mapped[int | None] = mapped_column(Integer, nullable=True)
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source_end: Mapped[int | None] = mapped_column(Integer, nullable=True)
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pinned: Mapped[bool] = mapped_column(Boolean, default=False)
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forgotten: Mapped[bool] = mapped_column(Boolean, default=False) # evicted, kept for UI
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use_count: Mapped[int] = mapped_column(Integer, default=0)
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last_used_at: Mapped[datetime | None] = mapped_column(DateTime, nullable=True)
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created_at: Mapped[datetime] = mapped_column(DateTime, default=utcnow)
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adventure: Mapped[Adventure] = relationship(back_populates="memories")
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@property
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def embedded(self) -> bool:
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return self.embedding is not None
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class StoryCard(Base):
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"""Owned by either a scenario or an adventure (exactly one set)."""
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__tablename__ = "story_cards"
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id: Mapped[int] = mapped_column(primary_key=True)
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scenario_id: Mapped[int | None] = mapped_column(
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ForeignKey("scenarios.id", ondelete="CASCADE"), nullable=True
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)
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adventure_id: Mapped[int | None] = mapped_column(
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ForeignKey("adventures.id", ondelete="CASCADE"), nullable=True
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)
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type: Mapped[str] = mapped_column(String(100), default="")
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name: Mapped[str] = mapped_column(String(200), default="")
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keys: Mapped[str] = mapped_column(Text, default="") # comma-separated triggers
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entry: Mapped[str] = mapped_column(Text, default="")
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notes: Mapped[str] = mapped_column(Text, default="")
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# Adventure copies only: which piece of the scenario this card came from —
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# "card:<scenario_card_id>" or "npc:<npc_key>". "Update from scenario"
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# refreshes/removes exactly these; NULL means player-authored (left alone),
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# or a copy predating the column (matched by name, then adopted).
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source_ref: Mapped[str | None] = mapped_column(String(64), nullable=True)
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scenario: Mapped[Scenario | None] = relationship(back_populates="story_cards")
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adventure: Mapped[Adventure | None] = relationship(back_populates="story_cards")
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class Action(Base):
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__tablename__ = "actions"
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id: Mapped[int] = mapped_column(primary_key=True)
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adventure_id: Mapped[int] = mapped_column(ForeignKey("adventures.id", ondelete="CASCADE"))
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index: Mapped[int] = mapped_column(Integer)
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type: Mapped[str] = mapped_column(String(20)) # start|do|say|story|continue|ai
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text: Mapped[str] = mapped_column(Text, default="")
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# Reasoning-model "thinking" that preceded the text (AI actions only).
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reasoning: Mapped[str | None] = mapped_column(Text, nullable=True)
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# The full assembled prompt for this turn, for the Insights viewer. By far
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# the biggest column in the database (~74 KB/row in production), and needed
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# by exactly one endpoint, one action at a time — so it is deferred: never
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# loaded unless something actually touches the attribute. Bulk readers must
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# NOT touch it; that is what `world_delta` below exists for.
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context_snapshot: Mapped[dict | None] = mapped_column(
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JSON, nullable=True, deferred=True
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)
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# The small slice of the snapshot that IS needed in bulk: this turn's RPG
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# state changes, for the inline chips under an AI message (world_changes)
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# and for re-attaching the emit block when replaying history to the model.
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# Mirrors the active variant, same as text/reasoning/context_snapshot.
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world_delta: Mapped[dict | None] = mapped_column(JSON, nullable=True)
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# Copy of Adventure.script_state as it was immediately BEFORE this action's
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# script hooks ran, so undo/retry can roll the shared scoreboard back.
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# NULL for actions created before this column existed. Deferred: only ever
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# read for the one action being undone or retried.
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state_before: Mapped[dict | None] = mapped_column(
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JSON, nullable=True, deferred=True
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)
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# Phase 12: same idea for the RPG world_state, so undo/retry rolls it back too.
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world_state_before: Mapped[dict | None] = mapped_column(
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JSON, nullable=True, deferred=True
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)
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# Retry history (AI actions): every attempt made for this turn, oldest
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# first, INCLUDING the active one. NULL/empty means never retried — the row
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# is its own only version. `variant_index` says which entry `text`,
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# `reasoning` and `context_snapshot` currently mirror; retry appends and
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# points here instead of deleting the row, so nothing is lost.
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#
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# Deferred for the same reason as context_snapshot: a list response only
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# ever needs the *count* (see variant_count below), but the column holds
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# every discarded attempt's full narration, so loading it in bulk made each
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# retry a permanent tax on every later page load of that adventure.
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variants: Mapped[list | None] = mapped_column(JSON, nullable=True, deferred=True)
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# len(variants), maintained on write by set_variants() so the deferred
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# column above never has to be fetched just to count it. 0 = never retried.
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variant_count: Mapped[int] = mapped_column(Integer, default=0)
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variant_index: Mapped[int] = mapped_column(Integer, default=0)
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created_at: Mapped[datetime] = mapped_column(DateTime, default=utcnow)
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adventure: Mapped[Adventure] = relationship(back_populates="actions")
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@property
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def world_changes(self) -> list[dict]:
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"""Compact per-turn RPG state changes (Phase 12), for the inline summary
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under an AI message. Labels are path-based (no schema needed):
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`npc.gwen.trust` -> "gwen trust".
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Reads `world_delta`, never `context_snapshot` — this runs for every
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action in a list response, and touching the deferred snapshot here
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would drag the whole prompt archive out of the database."""
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wd = self.world_delta if isinstance(self.world_delta, dict) else None
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if wd is None:
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return []
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applied = wd.get("applied") or []
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out: list[dict] = []
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for entry in applied:
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parts = str(entry.get("path", "")).split(".")
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section, name = parts[0], parts[-1]
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if section == "flags":
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out.append({"kind": "flag", "label": name, "on": bool(entry.get("new"))})
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elif section == "milestones":
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out.append({"kind": "milestone", "label": name})
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else:
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label = f"{parts[1]} {parts[2]}" if section == "npc" and len(parts) == 3 else name
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old, new = entry.get("old"), entry.get("new")
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delta = new - old if isinstance(old, (int, float)) and isinstance(new, (int, float)) else None
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out.append({"kind": "stat", "label": label, "delta": delta, "value": new})
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return out
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class Script(Base):
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__tablename__ = "scripts"
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id: Mapped[int] = mapped_column(primary_key=True)
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user_id: Mapped[int | None] = mapped_column(
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ForeignKey("users.id", ondelete="CASCADE"), nullable=True
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)
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name: Mapped[str] = mapped_column(String(200), default="Untitled Script")
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description: Mapped[str] = mapped_column(Text, default="")
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library_js: Mapped[str] = mapped_column(Text, default="")
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input_js: Mapped[str] = mapped_column(Text, default="")
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context_js: Mapped[str] = mapped_column(Text, default="")
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output_js: Mapped[str] = mapped_column(Text, default="")
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created_at: Mapped[datetime] = mapped_column(DateTime, default=utcnow)
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updated_at: Mapped[datetime] = mapped_column(DateTime, default=utcnow, onupdate=utcnow)
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class AdventureScript(Base):
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"""A script copied into an adventure at creation, so library edits don't
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change running adventures unless the player explicitly re-syncs it from
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`source_script_id`. `state` lives on Adventure.script_state (one shared
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state per adventure, as in AI Dungeon)."""
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__tablename__ = "adventure_scripts"
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id: Mapped[int] = mapped_column(primary_key=True)
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adventure_id: Mapped[int] = mapped_column(ForeignKey("adventures.id", ondelete="CASCADE"))
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# The library Script this copy was made from, so it can be re-synced on
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# demand. NULL for legacy copies (predate this column) and demo-derived
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# ones whose source isn't owned by the player — those fall back to a
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# name match, or simply aren't syncable.
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source_script_id: Mapped[int | None] = mapped_column(
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ForeignKey("scripts.id", ondelete="SET NULL"), nullable=True
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)
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position: Mapped[int] = mapped_column(Integer, default=0)
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enabled: Mapped[bool] = mapped_column(Boolean, default=True)
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name: Mapped[str] = mapped_column(String(200), default="Untitled Script")
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description: Mapped[str] = mapped_column(Text, default="")
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library_js: Mapped[str] = mapped_column(Text, default="")
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input_js: Mapped[str] = mapped_column(Text, default="")
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context_js: Mapped[str] = mapped_column(Text, default="")
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output_js: Mapped[str] = mapped_column(Text, default="")
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adventure: Mapped[Adventure] = relationship(back_populates="scripts")
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class Settings(Base):
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__tablename__ = "settings"
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id: Mapped[int] = mapped_column(primary_key=True)
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# Phase 8: one row per user (pre-Phase-8 DBs had a single id=1 row, which
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# the migration assigns to the local user).
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user_id: Mapped[int | None] = mapped_column(
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ForeignKey("users.id", ondelete="CASCADE"), nullable=True, unique=True
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)
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endpoint_url: Mapped[str] = mapped_column(String(500), default="http://localhost:11434/v1")
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# Fernet-encrypted at rest ("enc:..." — see security.py); use api_key_plain.
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api_key: Mapped[str] = mapped_column(String(500), default="")
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model: Mapped[str] = mapped_column(String(200), default="")
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api_mode: Mapped[str] = mapped_column(String(20), default="chat") # chat|completion
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temperature: Mapped[float] = mapped_column(Float, default=0.8)
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# 800 leaves room for a full scene; 400 tended to truncate mid-paragraph
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# and left reasoning models with nothing after their thinking.
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max_output_tokens: Mapped[int] = mapped_column(Integer, default=800)
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# Separate thinking budget for reasoning models (OpenRouter-style
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# `reasoning: {max_tokens}`); 0 = param not sent, -1 = reasoning explicitly
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# off (`reasoning: {effort: none}`). Added on top of
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# max_output_tokens so story output keeps its full budget.
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reasoning_max_tokens: Mapped[int] = mapped_column(Integer, default=0)
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context_token_budget: Mapped[int] = mapped_column(Integer, default=16384)
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narrator_prompt: Mapped[str] = mapped_column(
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Text,
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default=(
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"You are a masterful storyteller continuing an interactive adventure. "
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"Continue the story naturally in second person, staying consistent with "
|
|
"everything established so far. Write vivid prose. Never speak for the "
|
|
"player or break character. Do not conclude the story; always leave room "
|
|
"for the player's next action."
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|
),
|
|
)
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|
stream: Mapped[bool] = mapped_column(Boolean, default=True)
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|
# Phase 6: auto-summarization + memory bank
|
|
summary_model: Mapped[str] = mapped_column(String(200), default="") # "" = main model
|
|
embedding_model: Mapped[str] = mapped_column(String(200), default="") # "" = bank disabled
|
|
memory_bank_capacity: Mapped[int] = mapped_column(Integer, default=200)
|
|
memory_top_k: Mapped[int] = mapped_column(Integer, default=5)
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|
|
|
@property
|
|
def has_api_key(self) -> bool:
|
|
return bool(self.api_key)
|
|
|
|
@property
|
|
def api_key_plain(self) -> str:
|
|
from . import security # local import: models is imported before security
|
|
|
|
return security.decrypt_secret(self.api_key)
|