M9: a campaign you can actually get back

A campaign could already be exported and imported. What could not survive the
trip was everything that explains it: the state events behind the authoritative
document, the prompt each turn was actually given, the passages it was shown,
the summaries that carry long-story continuity, and which take belonged to which
turn. An imported campaign could be read and could no longer say why it was what
it was — and a manual correction, the one state change no narration explains,
was indistinguishable from something the story had established.

The bundle is now `ai-dnd-adventure-v3`, and the version is the design rather
than a side effect. Everything added here could have been another optional key,
the way persona, Save Points, narrative state and imported knowledge each were.
That mechanism stops working at exactly this addition: a v2 file with no prompt
provenance is ambiguous between "written before M9" and "written by M9 from a
campaign that has none", and those are different facts about a campaign. A
version number is how a recovery file states what it was capable of recording.
v1 and v2 still import, and every seam from pre-active-head onward is tested for
the rule that an older file is never reinterpreted under a newer assumption.

Two categories became three. "Chosen travels, derived is recomputed" was enough
until stored prompts had to be decided: they are derived, and they must travel
anyway. The test that separates evidence from cache is not "could this be
recomputed" but "would a recomputation answer the same question" — a rebuilt
search index answers the same question, a rebuilt prompt says what the turn
would be told *now*, which is the opposite of what the inspector is for.

Also here: a real SQLite backup, through the online backup API rather than a
file copy, taken while the application is running and verified before it is
kept; story cards settled as compatibility-only legacy data and taken out of the
narrator's prompt, because they were the untracked path around knowledge
authority that IMPORTED-KNOWLEDGE-DESIGN §73 already forbade; and no schema
change at all, proved against a database M8's own code wrote.

Three defects, found by running the milestone's own tests rather than by reading
them. Deleting a campaign leaked its FTS index rows, and SQLite then handed the
freed ids to the next source imported into any campaign, which failed with an
integrity error that Reindex could not repair — both ends are closed, and a
database already carrying the damage now repairs itself. An imported node with
no state snapshot was being stamped with the campaign's head state, so an Undo
to turn 2 showed what the story knew at turn 20. And the snapshot relink did not
persist at all, because it mutated a dict in place on a column SQLAlchemy tracks
by assignment: it looked correct in memory and wrote the wrong ids to disk.

Carrying per-turn prompts looked like it would halve the length of campaign that
can be restored. Measured — and after compressing them inside the file —
everything M9 added costs 12% of it: the import ceiling moves from about 318
turns to about 279, against a 100-turn certification target. The dominant cost
is not M9's at all. The per-position narrative state document is 74% of a
bundle, and v2 already carried it.

Backend 1,102 passed / 14 skipped / 0 failed. Frontend 145 passed. Lint,
production build and Docker build clean. Verified across two server processes
with two data directories, and in a real browser against a real narrator.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Qyn3oRd4D6pi72nKBG725B
This commit is contained in:
JesseMarkowitz
2026-09-07 01:55:45 -04:00
co-authored by Claude Opus 5
parent 1ce9972760
commit 44edece67e
46 changed files with 9227 additions and 178 deletions
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"""Measures what a campaign bundle preserves, omits and rebuilds.
python -m tools.m9_portability_report # human-readable
python -m tools.m9_portability_report --json # machine-readable
Run from `backend/`, with the virtualenv on the path. The script builds the M9
portability fixture in a throwaway database, exports it, imports it into a second
throwaway database, and then compares the two campaigns family by family.
It exists because the M9 brief asks for the baseline to be **measured** rather
than assumed. Running it on the M8 commit produces the inventory M9 started from;
running it on the M9 tree produces the one M9 finished with, and the difference
between the two files is the milestone's portability claim in a form a reviewer
can reproduce rather than take on trust.
The comparison is by data family rather than by row count. "12 actions in, 12
actions out" is the check that misses a bundle carrying every turn and none of
its state, so each family below reports what a reader could still see afterwards.
Nothing here touches the developer's own database: two temporary files are
created and removed, and no network call is made — the narrator, the summariser
and the embedder are all local fakes.
"""
from __future__ import annotations
import argparse
import json
import os
import sys
import tempfile
import time
from pathlib import Path
# The test harness owns the fixture and the fakes. Both live under `tests/`,
# which is not a package, so the path is extended rather than imported from.
_HERE = Path(__file__).resolve().parent
sys.path.insert(0, str(_HERE.parent / "tests"))
# `app.database` reads this at import and builds the engine once, exactly as
# `tests/conftest.py` explains. It has to be set before the first `app` import.
_SOURCE_DB = tempfile.NamedTemporaryFile(suffix="-m9-source.db", delete=False)
_SOURCE_DB.close()
os.environ["AIDND_DB_PATH"] = _SOURCE_DB.name
os.environ.pop("AIDND_DATABASE_URL", None)
os.environ.pop("DATABASE_URL", None)
from fastapi import Depends # noqa: E402
from fastapi.testclient import TestClient # noqa: E402
import m9_fixture # noqa: E402
from app import auth, limits, memorybank, models # noqa: E402
from app.database import Base, SessionLocal, engine, get_db # noqa: E402
from app.main import app # noqa: E402
from app.routers import adventures # noqa: E402
from fakes import ScriptedProvider # noqa: E402
class _StubDerivedProvider:
"""Deterministic vectors and prose, so the report needs no model at all.
One object serves as both the embedder and the summariser, because the
memory pass builds each from the same factory and stubbing only one of them
is the M6 finding M6-F3 mistake: the unstubbed factory opens a socket
against the default endpoint on every turn.
"""
_written = 0
async def complete(self, system, prompt, **kwargs):
_StubDerivedProvider._written += 1
return (
f"Memory {_StubDerivedProvider._written}: what the story had "
f"established by this point."
)
async def embed(self, texts):
out = []
for text in texts:
lowered = text.lower()
out.append([
1.0,
1.0 if "abbey" in lowered or "crypt" in lowered else 0.0,
1.0 if "tavern" in lowered or "lantern" in lowered else 0.0,
1.0 if "rain" in lowered else 0.0,
])
return out
def _install_fakes() -> None:
adventures.turns.OpenAICompatibleProvider = ScriptedProvider
memorybank.embedding_provider = lambda s: _StubDerivedProvider()
memorybank.summary_provider = lambda s: _StubDerivedProvider()
limits.check_row_cap = lambda *a, **k: None
def _new_user_and_campaign(title: str) -> tuple[int, int]:
db = SessionLocal()
try:
user = models.User(is_guest=False, email=f"m9-{title}@example.com")
db.add(user)
db.flush()
db.add(models.Settings(
user_id=user.id, model="report-model", embedding_model="stub-embed",
context_token_budget=4000, max_output_tokens=400,
))
adventure = models.Adventure(
user_id=user.id, title=title,
campaign_canon=m9_fixture.CAMPAIGN_CANON,
)
db.add(adventure)
db.flush()
db.add(models.Action(
adventure_id=adventure.id, type="start", text=m9_fixture.OPENING,
))
# A neighbour, so a bundle that reached past its own campaign would
# bring back rows this report can see.
neighbour = models.Adventure(user_id=user.id, title="Neighbour")
db.add(neighbour)
db.flush()
db.add(models.Action(
adventure_id=neighbour.id, type="start", text="A different story.",
))
db.commit()
return adventure.id, user.id
finally:
db.close()
def _client(user_id: int) -> TestClient:
app.dependency_overrides[auth.get_current_user] = (
lambda db=Depends(get_db): db.get(models.User, user_id)
)
return TestClient(app)
# ----------------------------------------------------------------- the families
# One entry per data family the M9 brief asks the baseline to classify. Each
# `present` function answers "did this survive into the file?" from the bundle
# alone, because that is the question the classification is about.
def _actions(bundle: dict) -> list[dict]:
return [a for a in (bundle.get("actions") or []) if isinstance(a, dict)]
def _snapshots(bundle: dict) -> list[dict]:
"""Every stored prompt in the file, decoded.
The export compresses them (`bundle._packed`), so a report that looked for a
plain dict would say the evidence was omitted when it is merely encoded —
which is the mistake this whole tool exists to avoid making about anything.
"""
from app import bundle as bundle_module
out = []
for action in _actions(bundle):
snapshot = (
action.get("contextSnapshot")
if isinstance(action.get("contextSnapshot"), dict)
else bundle_module._unpacked(action.get("contextSnapshotZ"))
)
if isinstance(snapshot, dict):
out.append(snapshot)
return out
FAMILIES: list[tuple[str, str, callable]] = [
("campaign identity",
"title, instructions, persona, canon, the campaign's own settings",
lambda b: bool(b.get("title"))),
("transcript",
"every accepted player and narrator action, live and superseded",
lambda b: bool(_actions(b))),
("branches",
"the retained tree, its fork points and its names",
lambda b: bool(b.get("branches"))),
("branch disposition",
"which lines the story left behind, and where",
lambda b: any("supersededAt" in x for x in (b.get("branches") or []))),
("active head",
"the branch and depth the campaign is being read at",
lambda b: b.get("headDepth") is not None),
("alternate takes",
"every attempt at a turn, and which one is the story",
lambda b: any(not a.get("live", True) for a in _actions(b))),
("take grouping",
"which attempts belong to the same turn across a fork (SP9 parentage)",
lambda b: any("parentId" in a for a in _actions(b))),
("save points",
"named coordinates, their notes and their positions",
lambda b: bool(b.get("checkpoints"))),
("narrative state (current)",
"the authoritative document at the exported head",
lambda b: b.get("narrativeState") is not None),
("narrative state (per position)",
"the snapshot every position restores from",
lambda b: any("narrativeStateAfter" in a for a in _actions(b))),
("state events",
"the accepted typed events: the audit half of the hybrid",
lambda b: bool(b.get("stateEvents"))),
("state proposals",
"what the model proposed and what the application did about it",
lambda b: bool(b.get("stateProposals"))),
("manual corrections",
"state the user asserted, distinguishable from state the story did",
lambda b: any(e.get("source") == "manual_correction"
for e in (b.get("stateEvents") or []))),
("historical prompt/context",
"the exact prompt each turn was given",
lambda b: bool(_snapshots(b))),
("retrieval provenance",
"which passages a historical turn was shown, and their text",
lambda b: any((s.get("knowledge") or {}).get("used") for s in _snapshots(b))),
("per-turn model settings",
"the model and generation settings a historical turn ran under",
lambda b: any(s.get("settings") for s in _snapshots(b))),
("imported knowledge",
"source content, class, lifecycle, visibility and hash",
lambda b: bool(b.get("knowledge"))),
("knowledge parser versions",
"what produced the chunks the source last had",
lambda b: any("parserVersion" in k for k in (b.get("knowledge") or []))),
("summaries",
"the generated rolling summaries and the story they cover",
lambda b: bool(b.get("summaries"))),
("memories",
"long-term memories and the coordinate each hangs off",
lambda b: bool(b.get("memories"))),
("memory authority",
"whether a memory is accepted story or a heuristic reading of it",
lambda b: any("authority" in m for m in (b.get("memories") or []))),
("scene metadata",
"the scene section of the authoritative state document",
lambda b: isinstance(b.get("narrativeState"), dict)
and "scene" in b["narrativeState"]),
("story cards (legacy)",
"the inherited lore primitive, which has no v1 browser surface",
lambda b: "storyCards" in b),
]
#: Families that are deliberately rebuilt rather than carried, with the reason.
REBUILDABLE = {
"knowledge passages": "a deterministic function of the source content",
"lexical (FTS) index": "rebuilt from the passages on import",
"knowledge embeddings": "belong to the importing machine's embedding model",
"memory embeddings": "the same, for the memory bank",
"branch lineage cache": "computed from parent plus fork depth",
"derived status": "describes the last run of a background pass, not the story",
}
def measure(json_out: bool) -> dict:
_install_fakes()
Base.metadata.create_all(bind=engine)
adv_id, user_id = _new_user_and_campaign("M9 Portability Fixture")
client = _client(user_id)
built = time.perf_counter()
source = m9_fixture.build(client, adv_id)
build_seconds = time.perf_counter() - built
started = time.perf_counter()
response = client.get(f"/api/adventures/{adv_id}/export")
export_seconds = time.perf_counter() - started
response.raise_for_status()
bundle = response.json()
encoded = json.dumps(bundle, ensure_ascii=False).encode("utf-8")
started = time.perf_counter()
imported = client.post("/api/adventures/import", json=bundle)
import_seconds = time.perf_counter() - started
import_status = imported.status_code
copy = (
m9_fixture.snapshot_of(client, imported.json()["id"])
if import_status == 201 else None
)
source_db_bytes = Path(_SOURCE_DB.name).stat().st_size
app.dependency_overrides.clear()
families = [
{"family": name, "what": what,
"verdict": "PRESERVED" if present(bundle) else "OMITTED"}
for name, what, present in FAMILIES
]
report = {
"format": bundle.get("format"),
"families": families,
"rebuildable": REBUILDABLE,
"sizes": {
"source_database_bytes": source_db_bytes,
"bundle_bytes": len(encoded),
"bundle_actions": len(_actions(bundle)),
"bundle_keys": sorted(bundle),
},
"timings_seconds": {
"fixture_build": round(build_seconds, 3),
"export": round(export_seconds, 3),
"import": round(import_seconds, 3),
},
"round_trip": {
"import_status": import_status,
"agrees": _agreement(source, copy) if copy else None,
},
}
return report
def _agreement(source: dict, copy: dict) -> dict:
"""Which of the reader-visible families match between original and copy."""
keys = ("title", "canon_rules", "transcript", "branch_count", "checkpoints",
"knowledge", "state", "state_events", "memories", "summaries",
"can_undo", "can_redo")
return {key: source.get(key) == copy.get(key) for key in keys}
def main() -> int:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--json", action="store_true",
help="print the report as JSON")
args = parser.parse_args()
try:
report = measure(args.json)
finally:
for path in (_SOURCE_DB.name,):
try:
os.unlink(path)
except OSError:
pass
if args.json:
print(json.dumps(report, indent=2, sort_keys=True))
return 0
print(f"bundle format: {report['format']}")
print(f"bundle size: {report['sizes']['bundle_bytes']:,} bytes "
f"across {report['sizes']['bundle_actions']} actions")
print(f"source db: {report['sizes']['source_database_bytes']:,} bytes")
print(f"timings: {report['timings_seconds']}")
print()
width = max(len(name) for name, _, _ in FAMILIES)
for row in report["families"]:
print(f" {row['verdict']:<10} {row['family']:<{width}} {row['what']}")
print()
print(" DERIVED/REBUILDABLE (deliberately not carried)")
for name, why in REBUILDABLE.items():
print(f" {name:<24} {why}")
print()
print(f"round trip: HTTP {report['round_trip']['import_status']}")
for key, agreed in (report["round_trip"]["agrees"] or {}).items():
print(f" {'same' if agreed else 'DIFFERS':<8} {key}")
return 0
if __name__ == "__main__":
raise SystemExit(main())