Two reads still grew without bound after the snapshot fix. `Action.variants` holds every discarded retry attempt, but a list response only needs how many there are — so each retry permanently added ~5 KB to every later load of that adventure. Defer the column and keep the count beside it (migration 37, backfilled server-side), with set_variants() as the one write path that keeps the two in step. `story_actions()` walked adventure.actions, then every caller threw almost all of it away: the builder concatenates the story and immediately cuts it back to the token budget, the NPC check looks at the last 6, retrieval at the last 4, the cursor clamp only wants a count. A turn on a 200-action adventure read 839 KB to use ~70 KB, and grew with every turn played. app/context/ history.py serves those shapes from SQL; window_covering() measures the actions it fetched and projects how many more it needs, fetching only the part it does not already hold. Memorybank cursors move to position_of_index() and settled_count()/settled_slice() — same arithmetic, no full list. The scripting pipeline still receives the whole history per AI Dungeon's API, and every helper reuses adventure.actions when it is already loaded, so a scripted adventure pays what it always did and never twice. Measured at production shape: retry tax 5.1 KB -> 0; turn 200 839 KB -> 129 KB and flat from ~turn 50; a 200-turn playthrough 84.5 MB -> 23.0 MB; a delete 115 KB -> 5 KB. Verified the window builds a byte-identical prompt to the full story across budgets from 1K to 100K tokens, with and without the retry exclusion - this is a cost change and nothing else. Cursor helpers checked against the old list arithmetic, including after deleting a middle action. Counts are real SELECT count(...): Query.count() wraps the entity select in a subquery, so the SQL named every deferred column and the egress guard could not tell it apart from a bulk fetch. 139 tests pass; the four new guards verified by sabotage. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01UeQVy5bEjLhfgWNc27Efet
283 lines
12 KiB
Python
283 lines
12 KiB
Python
"""Context assembly per AI Dungeon's memory system
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(help.aidungeon.com/faq/the-memory-system):
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[AI Instructions] always included
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[Plot Essentials] always included (classic "Memory")
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[Story Summary] always included (manual in Phase 3, auto in Phase 6)
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[Used Memories] top-K memory-bank retrievals (Phase 6, when enabled)
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[Triggered Story Cards] "World Lore: <entry>", conditional; first dropped when over budget
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[Story history] newest actions that fit the remaining token budget
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[Author's Note] injected AUTHORS_NOTE_DEPTH actions before the end of history
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[Latest player action] (+ script frontMemory right after it, Phase 4)
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"""
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import functools
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from dataclasses import dataclass
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import tiktoken
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from .. import models, worldstate
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from . import history
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AUTHORS_NOTE_DEPTH = 3 # actions from the end of history
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CARD_BUDGET_SHARE = 0.4 # max share of non-reserved budget that story cards may take
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NPC_WINDOW = 6 # actions of story searched for NPC trigger words ("in scene")
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SEPARATOR = "\n\n"
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@functools.lru_cache(maxsize=1)
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def _encoding() -> tiktoken.Encoding:
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return tiktoken.get_encoding("cl100k_base")
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def count_tokens(text: str) -> int:
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return len(_encoding().encode(text))
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def truncate_to_last_tokens(text: str, budget: int) -> str:
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tokens = _encoding().encode(text)
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if len(tokens) <= budget:
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return text
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return _encoding().decode(tokens[-budget:])
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@dataclass
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class Section:
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label: str
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text: str
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@property
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def tokens(self) -> int:
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return count_tokens(self.text)
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def _script_memory(adventure: models.Adventure) -> dict:
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"""Script-provided memory overrides (populated by Phase 4 scripting)."""
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state = adventure.script_state if isinstance(adventure.script_state, dict) else {}
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memory = state.get("memory")
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return memory if isinstance(memory, dict) else {}
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def _history_text(action: models.Action) -> str:
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"""An AI turn as the model should see it in replayed history: its narration
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with the state block it emitted re-appended (reconstructed from the stored
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delta). The block is stripped before storage/UI, so without this every past
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AI turn would look like one that emitted nothing — biasing the model, by
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imitation, to stop emitting too. Player turns and blockless turns are
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returned unchanged.
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Reads `world_delta`, not `context_snapshot`: this runs for every action in
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the replayed history, and the snapshot is deferred precisely so a turn
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never drags the prompt archive out of the database."""
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text = action.text
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wd = action.world_delta if isinstance(action.world_delta, dict) else None
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if wd:
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block = worldstate.render_delta_block(wd.get("delta") or {})
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if block:
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text = f"{text}\n{block}"
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return text
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def _visible_npcs(actions: list[models.Action], stat_schema: dict) -> dict[str, str]:
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"""Defined NPCs whose trigger words appear in the recent story — the ones
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"in scene", so only their stats get injected. Maps npc id -> display name.
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`actions` is already the last handful (see NPC_WINDOW)."""
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recent = SEPARATOR.join(a.text for a in actions).lower()
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visible: dict[str, str] = {}
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for npc_key, ndef in (stat_schema.get("npcs") or {}).items():
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if not isinstance(ndef, dict):
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continue
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if any(trigger in recent for trigger in worldstate.npc_triggers(ndef, npc_key)):
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visible[npc_key] = worldstate.npc_name(ndef, npc_key)
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return visible
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def _match_cards(cards: list[models.StoryCard], window_text: str) -> list[dict]:
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"""AI Dungeon trigger rules: case-insensitive, space-sensitive, partial-word
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('boat' triggers on 'boats'). Returns one record per card with the keyword that fired."""
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haystack = window_text.lower()
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matched = []
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for card in cards:
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for key in (k.strip().lower() for k in card.keys.split(",")):
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if key and key in haystack:
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matched.append(
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{"id": card.id, "name": card.name, "keyword": key, "entry": card.entry}
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)
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break
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return matched
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def build_context(
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adventure: models.Adventure,
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settings: models.Settings,
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memory_bank: dict | None = None,
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exclude_action_id: int | None = None,
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) -> tuple[str, str, dict]:
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"""Returns (system_text, story_text, context_report). `memory_bank` is the
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result of memorybank.retrieve_memories (None when the bank is off);
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`exclude_action_id` omits one action from the story (see history.py)."""
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script_mem = _script_memory(adventure)
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# ----- Always-included components -----
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system_sections: list[Section] = [Section("narrator", settings.narrator_prompt.strip())]
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# RPG world state (Phase 12): current stats/milestones + how to report changes.
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stat_schema = adventure.scenario.stat_schema if adventure.scenario else None
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has_ws = worldstate.has_schema(stat_schema)
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if has_ws:
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guide = worldstate.render_reference(stat_schema)
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if guide:
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system_sections.append(Section("world_state_guide", guide))
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block = worldstate.render_state_section(
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adventure.world_state, stat_schema,
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_visible_npcs(
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history.tail(adventure, NPC_WINDOW, exclude_action_id), stat_schema
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),
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)
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if block:
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system_sections.append(Section("world_state", block))
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system_sections.append(Section("world_state_rule", worldstate.EMIT_RULE))
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if isinstance(script_mem.get("context"), str) and script_mem["context"].strip():
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system_sections.append(Section("script_context", script_mem["context"].strip()))
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if adventure.ai_instructions.strip():
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system_sections.append(Section("ai_instructions", adventure.ai_instructions.strip()))
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if adventure.memory.strip():
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system_sections.append(
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Section("plot_essentials", f"Plot essentials:\n{adventure.memory.strip()}")
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)
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if adventure.story_summary.strip():
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system_sections.append(
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Section("story_summary", f"Story summary:\n{adventure.story_summary.strip()}")
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)
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if memory_bank and memory_bank.get("used"):
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lines = "\n".join(f"- {m['text']}" for m in memory_bank["used"])
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system_sections.append(Section("used_memories", f"Memories:\n{lines}"))
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authors_note_text = adventure.authors_note.strip()
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if isinstance(script_mem.get("authorsNote"), str) and script_mem["authorsNote"].strip():
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authors_note_text = script_mem["authorsNote"].strip()
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authors_note = f"[Author's note: {authors_note_text}]" if authors_note_text else ""
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front_memory = ""
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if isinstance(script_mem.get("frontMemory"), str):
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front_memory = script_mem["frontMemory"].strip()
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reserved = (
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sum(s.tokens for s in system_sections)
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+ count_tokens(authors_note)
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+ count_tokens(front_memory)
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+ (count_tokens(worldstate.EMIT_REMINDER) if has_ws else 0)
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)
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available = max(256, settings.context_token_budget - reserved)
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# Only the newest actions can reach the prompt: everything below is either
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# truncated to `available` tokens or stops at the budget. Fetch a window
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# that is provably larger than that and no more — a long adventure would
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# otherwise read its entire history every turn to use the tail of it.
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actions = history.window_covering(
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adventure, available, count_tokens, exclude_action_id
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)
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# ----- Story cards: triggered by recent story text (the window history could fill) -----
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trigger_window = truncate_to_last_tokens(SEPARATOR.join(a.text for a in actions), available)
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triggered = _match_cards(adventure.story_cards, trigger_window)
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card_budget = int(available * CARD_BUDGET_SHARE)
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card_records = []
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lore_lines: list[str] = []
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used = 0
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for match in triggered:
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line = f"World Lore: {match['entry'].strip()}"
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tokens = count_tokens(line)
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included = used + tokens <= card_budget
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if included:
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lore_lines.append(line)
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used += tokens
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card_records.append(
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{"id": match["id"], "name": match["name"], "keyword": match["keyword"],
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"included": included}
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)
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if lore_lines:
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system_sections.append(Section("world_lore", "\n".join(lore_lines)))
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# ----- Story history: newest first until the remaining budget is spent -----
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history_budget = available - used
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included_actions: list[models.Action] = []
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spent = 0
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oldest_truncated = False
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for action in reversed(actions):
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# Budget on the text as it will actually appear — with the re-attached
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# state block (B) when this adventure tracks world state.
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rendered = _history_text(action) if has_ws else action.text
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tokens = count_tokens(rendered) + count_tokens(SEPARATOR)
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if spent + tokens > history_budget:
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if not included_actions:
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# Even the newest action alone is over budget: hard-truncate it.
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included_actions.append(
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models.Action(
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adventure_id=action.adventure_id, index=action.index,
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type=action.type,
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text=truncate_to_last_tokens(action.text, history_budget),
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)
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)
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oldest_truncated = True
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break
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included_actions.append(action)
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spent += tokens
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included_actions.reverse()
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# ----- Assemble story text with author's note near the end -----
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# Re-attach each AI turn's state block (stripped before storage) so recent
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# history shows the model its own emit pattern to imitate.
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texts = [_history_text(a) if has_ws else a.text for a in included_actions]
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note_sections: list[Section] = []
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if authors_note:
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pos = max(0, len(texts) - AUTHORS_NOTE_DEPTH)
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before, after = texts[:pos], texts[pos:]
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if before:
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note_sections.append(Section("history", SEPARATOR.join(before)))
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note_sections.append(Section("authors_note", authors_note))
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note_sections.append(Section("recent_history", SEPARATOR.join(after)))
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else:
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note_sections.append(Section("history", SEPARATOR.join(texts)))
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if front_memory:
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note_sections.append(Section("front_memory", front_memory))
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if has_ws:
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# Terminal reminder: the emit rule sits up in the system block, far from
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# where the model generates; repeat it last, in the strongest recency slot.
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note_sections.append(Section("world_state_reminder", worldstate.EMIT_REMINDER))
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story_sections = [s for s in note_sections if s.text]
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system_text = SEPARATOR.join(s.text for s in system_sections if s.text)
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story_text = SEPARATOR.join(s.text for s in story_sections)
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all_sections = [s for s in system_sections if s.text] + story_sections
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report = {
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"sections": [
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{"label": s.label, "text": s.text, "tokens": s.tokens} for s in all_sections
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],
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"prompt": {"system": system_text, "story": story_text},
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"tokens": {
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"total": count_tokens(system_text) + count_tokens(story_text),
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"budget": settings.context_token_budget,
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},
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"cards": card_records,
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"memories": memory_bank,
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"history": {
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"included": len(included_actions),
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# The whole story, not just the window fetched above — Insights
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# reports "N of M actions included" and M is the real total.
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"total": history.count(adventure, exclude_action_id),
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"oldest_truncated": oldest_truncated,
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},
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"settings": {
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"model": settings.model,
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"api_mode": settings.api_mode,
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"temperature": settings.temperature,
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"max_output_tokens": settings.max_output_tokens,
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},
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}
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return system_text, story_text, report
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