The post-implementation review of M2, plus the three corrections it took
to make the evidence true. Reports:
planning/reports/M2-BASELINE-REPORT.md 868 lines, the measurements
planning/reports/M2-IMPLEMENTATION-REPORT.md 758 lines, the reading of them
Verdict is PASS, accept with non-blocking debt, proceed to M3. Every M2
requirement is met and the ones that matter were tested by running the
build rather than reading it: a cloud endpoint written straight into
SQLite with sqlite3, behind the API's back, still refused at the wire;
trusted-LAN HTTPS against the real second machine with verification on;
captures showing zero packets outside loopback and the approved host.
Three defects, all found by running the shipped image.
The memory bank was dead. M2 removed Settings.api_key_plain with the API
key, and memorybank's two provider factories still read it. It failed
inside a fire-and-forget task, so no user error, no log anyone would
read, and no test — every memory test stubs those factories. All 604
tests passed with summaries and embeddings silently not happening.
The configurable model timeout never reached the turn engine. Stored,
validated, exposed in the API, rendered in the UI, and not passed to the
provider. M2's own exit criterion was half met: the constant had moved
but the setting did nothing.
And requirements.lock still pinned quickjs, psycopg and cryptography, so
the setup path DEVELOPMENT.md gives a new developer would have
reinstalled all three.
Both code defects now have the test that would have caught them: one
constructs every provider factory from a real Settings row, one drives
the turn endpoint, the chat endpoint and the summariser and asserts the
configured timeout arrives at each. That is the lesson worth keeping from
this milestone — after removing an attribute, build each consumer from a
real object; after adding a setting, prove it lands. Both failures were
in background or plumbing paths, which is exactly where a subtractive
change cannot see itself.
606 tests pass, up from 604. Lint, build and image are clean. Every
runtime result in the baseline report came from an image built after
these fixes; the reports say plainly that commit 8c65ae9 itself does not
contain them.
Also recorded: 88 test node IDs disappeared and every one is accounted
for — 64 whole files whose subject was removed, 5 replaced by a better
file, 15 individually retired with their features, and 4 renames. No
meaningful coverage was lost, and the eight files that used a JavaScript
counter as instrumentation kept their assertions by moving the counter to
the world-state engine.
Six planning recommendations are reported, not applied. Three are marked
before M3: the threat model still describes the inherited SSRF guard's
opposite rule, TECHNICAL-DESIGN §5.1 still marks two hardening items
open, and the endpoint policy is a load-bearing security decision that
exists only as a module docstring and deserves an ADR.
M3 is clear to start. Its chokepoints are untouched or simplified — the
rollback paths now carry one shared state instead of two — and the Phase
0B undo/redo spike still applies. No M3 work here: Undo still deletes and
there is still no Redo.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017foPNqFjAJa2Ngebf5mEfL
344 lines
14 KiB
Python
344 lines
14 KiB
Python
"""Playing a turn: the model call, the SSE stream, and the one-turn-at-a-time lock.
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Everything a test needs to intercept lives here, and other modules reach it as
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`turns.<name>` rather than importing it by value. That matters twice. The turn
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lock guards one set only while one module owns it. And a test that replaces
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`OpenAICompatibleProvider` or `generate_turn` patches this module, which every
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caller reads through.
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"""
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import threading
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from fastapi import Depends, HTTPException, Request
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from fastapi.responses import StreamingResponse
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from sqlalchemy.orm import Session
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from ... import (
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attempts, limits, memorybank, models, schemas, tree, worldstate,
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)
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from ...context import build_context, cursors
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from ...database import get_db
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from ...providers import OpenAICompatibleProvider, PromptParts, ProviderError
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from ...sse import SSE_HEADERS, sse, turn_error
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from ..settings import get_settings
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from .deps import CurrentUser, current_adventure, router
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from .nodes import _move_to_after, next_depth
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from .paging import annotate_takes
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def world_delta_of(snapshot: dict | None) -> dict | None:
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"""Returns the bulk-read slice of a context snapshot, for `Action.world_delta`.
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`context_snapshot` is deferred because it holds the whole assembled prompt.
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The parts that every action needs get their own small column instead: the
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world-change chips, the emit block replayed into history, and the refusal
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note fed back to the model. Update this function wherever a snapshot is
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written.
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Carry all three report lists, not just `applied`. `Action.world_changes`
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marks a chip from `clamped` and builds its refusal chips from `rejected`,
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and `worldstate.refusals` reads both. Storing `applied` alone left every
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consumer unable to tell a refused change from one that worked, which is the
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distinction this column exists to carry. The two extra lists are subsets of
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one turn's block, so they cost a few hundred bytes per action at most.
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"""
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ws = (snapshot or {}).get("world_state")
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if not isinstance(ws, dict):
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return None
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report = ws.get("report") or {}
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return {
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"delta": ws.get("delta") or {},
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"applied": report.get("applied") or [],
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"clamped": report.get("clamped") or [],
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"rejected": report.get("rejected") or [],
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}
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# One turn at a time per adventure. The set lives in memory, which is enough for
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# a single-process local app. Sync endpoints run in a threadpool, so the
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# check-and-add needs a lock. The check also has to run during the request
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# rather than when the SSE generator first runs. Otherwise two rapid requests
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# both pass the check and generate concurrently.
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_active_turns: set[int] = set()
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_active_turns_guard = threading.Lock()
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def acquire_turn_lock(adventure_id: int):
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"""Claims the adventure's turn slot atomically. `with_turn_lock` releases it."""
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with _active_turns_guard:
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if adventure_id in _active_turns:
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raise HTTPException(409, "A turn is already generating for this adventure.")
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_active_turns.add(adventure_id)
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async def with_turn_lock(adventure_id: int, gen):
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"""Wraps an SSE generator so that it releases the `acquire_turn_lock` lock."""
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try:
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async for event in gen:
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yield event
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finally:
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_active_turns.discard(adventure_id)
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def format_player_input(action_type: str, text: str) -> str:
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"""Formats player input the way AI Dungeon does."""
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text = text.strip()
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if action_type == "say":
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text = text.strip('"')
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if text and text[-1] not in ".!?…":
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text += "."
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return f'> You say "{text}"'
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if action_type == "do":
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if text.lower().startswith("you "):
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text = text[4:]
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if text and text[-1] not in ".!?…":
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text += "."
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return f"> You {text}"
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return text # The "story" type is appended as raw text.
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def action_json(action: models.Action, db: Session | None = None) -> dict:
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"""Serializes one action for the wire.
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Passing `db` fills in the pager numbers, and a turn that was just played must
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pass it. The attempt that turn created is often the second one at its
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coordinate, so the message needs a pager that the client cannot infer from a
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count of one. Without `db`, a retry showed no pager until the page reloaded.
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The adventure GET has the same requirement and builds its window a third way.
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"""
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if db is not None:
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annotate_takes(db, action.adventure_id, [action])
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return schemas.ActionOut.model_validate(action).model_dump(mode="json")
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async def generate_turn(
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adventure: models.Adventure,
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db: Session,
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user: models.User,
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retry_of: models.Action | None = None,
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):
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"""Streams the AI continuation as SSE, then stores the result.
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If `retry_of` is set, the result is stored as a sibling of that AI action, at
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the same turn and the same coordinate, and the discarded attempt stays where
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it was written. Before calling, the caller must roll the adventure back to
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the state before that turn. See `retry_action`. If this generator ends
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without saving, the rollback is undone, so the state cannot diverge from the
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text on screen.
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"""
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saved = False
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try:
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async for event in _generate_turn(adventure, db, user, retry_of):
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if event is _SAVED:
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saved = True
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continue
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yield event
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finally:
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if retry_of is not None and not saved:
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# The turn failed with a provider error, an empty reply, or a
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# disconnected client. No sibling was written, so the
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# attempt on screen is still the live one. Restore the state it
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# produced.
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attempts.restore_state(adventure, retry_of)
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db.commit()
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# `_generate_turn` yields this sentinel once the action is committed. It tells
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# the wrapper above to leave the rollback in place rather than reverse it.
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_SAVED = object()
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async def _generate_turn(
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adventure: models.Adventure,
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db: Session,
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user: models.User,
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retry_of: models.Action | None = None,
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):
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settings = get_settings(db, user)
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# On a retry, the attempt being replaced is still the live node of its turn,
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# because it stays live until a replacement exists. Filter it out of the
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# context. Otherwise the model reads the attempt it is replacing as
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# established story and writes a sequel to it.
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replacing_id = retry_of.id if retry_of is not None else None
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memories = await memorybank.retrieve_memories(
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adventure, settings, update_stats=True, exclude_action_id=replacing_id
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)
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system_text, story_text, snapshot = build_context(
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adventure, settings, memories, exclude_action_id=replacing_id
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)
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parts = PromptParts(system=system_text, story=story_text)
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provider = OpenAICompatibleProvider(
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settings.endpoint_url, settings.model, settings.api_mode,
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settings.model_timeout_seconds,
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)
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chunks: list[str] = []
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reasoning_chunks: list[str] = []
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try:
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async for kind, chunk in provider.generate(
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parts, temperature=settings.temperature, max_tokens=settings.max_output_tokens
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):
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if kind == "reasoning":
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reasoning_chunks.append(chunk)
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yield sse({"type": "reasoning", "text": chunk})
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else:
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chunks.append(chunk)
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yield sse({"type": "chunk", "text": chunk})
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except ProviderError as exc:
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yield turn_error(str(exc))
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return
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text = "".join(chunks).strip()
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# The model's literal reply, kept for the Insights "Raw AI output" view. It
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# still contains the world-state block, which the code below strips.
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raw_output = text
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if not text:
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# The model streamed reasoning but no story text, so it spent its whole
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# budget on reasoning. Report that rather than "empty response".
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if reasoning_chunks:
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detail = (
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"The model used its entire token budget on reasoning and returned no "
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'story text. Raise "Max output tokens" in Settings, set a "Reasoning '
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'max tokens" cap, or switch to a non-reasoning model.'
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)
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else:
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detail = "The AI returned an empty response."
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yield turn_error(detail)
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return
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# RPG world state (Phase 12): read the AI's state delta out of the reply,
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# apply it through the engine, and strip the block from the displayed text.
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#
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# A retry re-runs the same turn, so it is played at that turn's depth. The
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# cooldown rules run on a position in the story, and a second attempt at turn
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# 12 is still turn 12. This was `retry_of.index`, which held the same number
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# until SP4. Depth stays correct once a branch has its own numbering.
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ai_depth = retry_of.depth if retry_of is not None else next_depth(adventure)
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stat_schema = adventure.scenario.stat_schema if adventure.scenario else None
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if worldstate.has_schema(stat_schema):
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text, delta = worldstate.extract_delta(text)
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if not text.strip():
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yield turn_error("The AI returned only a state update and no story text.")
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return
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new_world_state, ws_report = worldstate.apply_delta(
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adventure.world_state, stat_schema, delta, ai_depth
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)
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adventure.world_state = new_world_state
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snapshot["world_state"] = {"delta": delta, "report": ws_report, "state": new_world_state}
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snapshot["raw_output"] = raw_output
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# The cost the endpoint reports for the call, including how much of the
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# prompt came from cache rather than being billed in full. This is recorded
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# per attempt, next to the prompt it priced.
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snapshot["usage"] = provider.last_usage
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reasoning = "".join(reasoning_chunks).strip() or None
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ai_action = models.Action(
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adventure_id=adventure.id,
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depth=ai_depth,
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type="ai",
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text=text,
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reasoning=reasoning,
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context_snapshot=snapshot,
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world_delta=world_delta_of(snapshot),
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)
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attempts.snapshot_outcome(adventure, ai_action)
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if retry_of is not None:
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attempts.add_attempt(db, adventure, retry_of, ai_action)
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db.add(ai_action)
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# The text at this coordinate changed, so anything derived from it no
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# longer describes the story. Withdraw the memory attached to the node
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# and return that stretch to both passes. Before SP4 this code was
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# unreachable, because the summarizer held the newest action back until
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# a turn landed on top of it. `memorybank.SETTLE_SLACK` keeps a memory
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# off the tip again, for cost rather than for correctness, so this is
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# now the rare case: undo or delete can carry a summarized node back to
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# the tip, and then a retry of it lands here. See `memorybank`.
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memorybank.forget_node(db, adventure, retry_of)
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cursors.rewind_all(adventure, retry_of.branch_id, ai_depth - 1)
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# Flush so the new attempt has an id. The session does not autoflush,
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# and attempts page in id order, so a read taken before this point puts
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# the newest attempt nowhere.
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db.flush()
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else:
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tree.place_action(db, adventure, ai_action)
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db.add(ai_action)
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adventure.updated_at = models.utcnow()
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db.commit()
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db.refresh(ai_action)
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yield _SAVED
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yield sse({"type": "done", "action": action_json(ai_action, db)})
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# Phase 6: schedule summarization and embedding without waiting for them.
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# The task opens its own database session.
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memorybank.schedule_post_turn(adventure)
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async def run_player_turn(
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adventure: models.Adventure,
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db: Session,
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payload: schemas.ActionCreate,
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user: models.User,
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preformatted: bool = False,
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):
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"""Plays a player's turn: their action, then the reply to it.
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`preformatted` means the text already carries the `> You ...` conventions and
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is written as-is. That applies when the player retakes a turn they already
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played (SP9). The editor is seeded with the stored text, which is already
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formatted, and a plain edit puts that same text in the box and writes it back
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verbatim. Formatting it a second time produces `> You > You ...`.
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"""
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# An empty do, say, or story action behaves as a continue.
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if payload.type != "continue" and payload.text.strip():
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formatted = (
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payload.text.strip() if preformatted
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else format_player_input(payload.type, payload.text)
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)
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player_action = models.Action(
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adventure_id=adventure.id,
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depth=next_depth(adventure),
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type=payload.type,
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text=formatted,
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)
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# The state this node leaves behind. The AI turn after it starts here,
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# and a retry of that turn rolls back to here.
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attempts.snapshot_outcome(adventure, player_action)
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tree.place_action(db, adventure, player_action)
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db.add(player_action)
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db.commit()
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db.refresh(player_action)
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# The new action was added through its foreign key, so the loaded
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# `adventure.actions` collection is stale. Without this expire,
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# `build_context` for the AI action does not see the player action
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# that was just saved.
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db.expire(adventure, ["actions"])
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yield sse({"type": "player", "action": action_json(player_action, db)})
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async for event in generate_turn(adventure, db, user):
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yield event
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@router.post("/{adventure_id}/actions")
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def create_action(
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adventure_id: int,
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payload: schemas.ActionCreate,
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request: Request,
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db: Session = Depends(get_db),
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user: models.User = CurrentUser,
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adventure: models.Adventure = Depends(current_adventure),
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):
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limits.check_row_cap("actions", db, user, adventure=adventure)
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acquire_turn_lock(adventure_id)
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try:
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_move_to_after(db, adventure, payload.after_id)
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except BaseException:
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_active_turns.discard(adventure_id)
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raise
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return StreamingResponse(
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with_turn_lock(adventure_id, run_player_turn(adventure, db, payload, user)),
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media_type="text/event-stream",
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headers=SSE_HEADERS,
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)
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