The AI would sometimes stop emitting the `state` delta block once it missed a turn. Two compounding causes: the emit rule sat only in the system block (far from where the model generates), and the block was stripped before storage — so every replayed history turn looked blockless, biasing the model by imitation to stop emitting too. - EMIT_REMINDER: a one-line reminder appended last in the prompt (strongest recency slot), gated on has_ws and counted against the token budget. - render_delta_block + _history_text: re-attach each past AI turn's own delta block in replayed history (reconstructed from the stored snapshot delta), so the model always sees its emit format. Action.text stays clean, so UI, embeddings, and card/NPC trigger-matching are unaffected. History budgeting counts the augmented text so it can't overflow. Undo/retry untouched (read the separate world_state_before column). 46 tests. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01UWVyFKvqJGjfbXdibLgkMe
264 lines
11 KiB
Python
264 lines
11 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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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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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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text = action.text
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snap = action.context_snapshot if isinstance(action.context_snapshot, dict) else None
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if snap:
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ws = snap.get("world_state")
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if isinstance(ws, dict):
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block = worldstate.render_delta_block(ws.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(adventure: models.Adventure, 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 = [a for a in adventure.actions if a.text.strip()]
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recent = SEPARATOR.join(a.text for a in actions[-6:]).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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) -> 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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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, _visible_npcs(adventure, stat_schema)
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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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# ----- Story cards: triggered by recent story text (the window history could fill) -----
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actions = [a for a in adventure.actions if a.text.strip()]
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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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"total": len(actions),
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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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