interactive-story/backend/tools/v11_b1_memory.py

"""v1.1 WP-B.1: run the deterministic memory-retention scenarios, or diagnose a real campaign.

    # the deterministic scenarios, against an isolated database in --out
    .venv/bin/python -m tools.v11_b1_memory scenarios --out "$HOME/v11-evidence/b1/<label>"

    # the four stages for a finished real campaign (reads its database; embeds
    # the recall query with the campaign's own configured embedding model)
    AIDND_TEST_ENDPOINT=... AIDND_TEST_EMBED_MODEL=nomic-embed-text:latest \\
      .venv/bin/python -m tools.v11_b1_memory diagnose --db <campaign.db> \\
        --plant-depth 3 --out "$HOME/v11-evidence/b1/<label>"

Run from `backend/`. Nothing here changes memory behaviour; see
`tools/memory_diagnostic.py` for what is measured and what the stubs model.
"""

from __future__ import annotations

import argparse
import json
import os
import shutil
import sys
from pathlib import Path


def main() -> int:
    parser = argparse.ArgumentParser(description=__doc__.split("\n")[0])
    sub = parser.add_subparsers(dest="command", required=True)
    scen = sub.add_parser("scenarios")
    scen.add_argument("--out", required=True)
    scen.add_argument("--only", action="append", default=[])
    diag = sub.add_parser("diagnose")
    diag.add_argument("--db", required=True)
    diag.add_argument("--plant-depth", type=int, required=True)
    diag.add_argument("--out", required=True)
    args = parser.parse_args()

    out = Path(args.out)
    out.mkdir(parents=True, exist_ok=True)

    if args.command == "scenarios":
        db_path = out / "scenarios.db"
        if db_path.exists():
            db_path.unlink()
        os.environ["AIDND_DB_PATH"] = str(db_path)
    else:
        # A copy, so diagnosis never writes to the evidence database.
        copy = out / "diagnosed-copy.db"
        shutil.copy2(args.db, copy)
        os.environ["AIDND_DB_PATH"] = str(copy)
    os.environ.pop("AIDND_DATABASE_URL", None)
    os.environ.pop("DATABASE_URL", None)

    from tools import memory_diagnostic as md  # after the database is chosen

    if args.command == "scenarios":
        names = args.only or list(md.SCENARIOS)
        summary = {}
        for name in names:
            result = md.run_scenario(md.SCENARIOS[name])
            (out / f"{name}.json").write_text(json.dumps(result, indent=2, default=str))
            d = result.get("diagnosis") or {}
            summary[name] = {
                "verdict": d.get("verdict"),
                "isolation_ok": (result.get("isolation") or {}).get("ok"),
                "plant_depth": result.get("plant_depth"),
                "recall_depth": result.get("recall_depth"),
                "f_evicted_at_turn": (result.get("eviction") or {}).get("f_evicted_at_turn"),
            }
            print(f"{name:26} verdict={d.get('verdict')!s:26} "
                  f"isolation_ok={summary[name]['isolation_ok']} "
                  f"plant={result.get('plant_depth')} recall={result.get('recall_depth')}")
        (out / "summary.json").write_text(json.dumps(summary, indent=2))
        return 0

    import asyncio

    from sqlalchemy.orm import undefer

    from app import memorybank, models
    from app.database import SessionLocal

    endpoint = os.environ.get("AIDND_TEST_ENDPOINT", "")
    embed_model = os.environ.get("AIDND_TEST_EMBED_MODEL", "")
    with SessionLocal() as db:
        adventure = db.query(models.Adventure).order_by(models.Adventure.id).first()
        settings = db.query(models.Settings).filter_by(user_id=adventure.user_id).first()
        if endpoint:
            settings.endpoint_url = endpoint
        if embed_model:
            settings.embedding_model = embed_model
        recall_action = (db.query(models.Action)
                         .filter(models.Action.adventure_id == adventure.id,
                                 models.Action.type == "ai")
                         .options(undefer(models.Action.context_snapshot))
                         .order_by(models.Action.id.desc()).first())
        embed = memorybank.embedding_provider(settings).embed
        iso = md.isolation(db, adventure, md.FACT_F, args.plant_depth,
                           recall_snapshot=recall_action.context_snapshot,
                           recall_depth=recall_action.depth)
        diagnosis = asyncio.run(md.diagnose(db, adventure, settings, md.FACT_F, args.plant_depth,
                                            recall_action=recall_action, embed=embed))
        variants = {}
        memory_id = diagnosis["created"]["memory_id"]
        if memory_id is not None and not diagnosis.get("retained", {}).get("forgotten"):
            for label, query in (("recall_turn", diagnosis.get("ranked", {}).get("query", "")),
                                 ("paraphrase", md.PARAPHRASE_QUERY),
                                 ("unrelated", md.UNRELATED_QUERY)):
                ranking = asyncio.run(md.rank_bank(db, adventure, settings, query, embed))
                row = next((r for r in ranking["scored"] if r["memory_id"] == memory_id), None)
                variants[label] = {"rank": row and row["rank"], "of": len(ranking["scored"]),
                                   "similarity": row and row["similarity"],
                                   "selected": bool(row and row["selected"])}
        db.rollback()
    report = {"isolation": iso, "diagnosis": diagnosis, "ranking_variants": variants}
    (out / "diagnosis.json").write_text(json.dumps(report, indent=2, default=str))
    print(json.dumps({"isolation_ok": iso["ok"], "verdict": diagnosis["verdict"]}, indent=2))
    return 0


if __name__ == "__main__":
    sys.exit(main())