interactive-story/backend/app/memorybank.py

"""Phase 6 — auto summarization + embedding memory bank
(per help.aidungeon.com/faq/the-memory-system).

After each turn, a fire-and-forget task (`run_post_turn`) runs with its own DB
session:
  - every MEMORY_INTERVAL actions (starting at MEMORY_START), each uncovered
    block of actions is summarized into a short "memory". Summarization only
    ever reads *settled* actions (see settled_story_actions) — the newest action
    is held back one turn because it is still retryable;
  - every SUMMARY_INTERVAL actions, the Story Summary is rewritten folding in
    the new memories (the user-edited text is always the base, never clobbered);
  - new memories are embedded (OpenAI-compatible /v1/embeddings) and the bank
    is evicted down to capacity ("forgotten" memories are kept for the UI).

At generation time, `retrieve_memories` embeds the recent story text and ranks
the bank by cosine similarity; the top-K become the "Memories" context section.

All AI calls here are best-effort: failures are logged (debug page) and retried
on a later turn because the cursors only advance on success.
"""

import asyncio
from array import array
from collections import OrderedDict

from sqlalchemy import func, select, update
from sqlalchemy.orm import Session, object_session

from . import models, tree, vectors
from .context import history, story_actions, truncate_to_last_tokens
from .database import SessionLocal
from .providers import OpenAICompatibleProvider, ProviderError
from .vectors import cosine  # re-exported: the ranking lives here, the maths there

MEMORY_INTERVAL = 6  # actions per memory
MEMORY_START = 12  # first memory once the adventure reaches this many actions
SUMMARY_INTERVAL = 15  # actions between Story Summary updates
MAX_MEMORIES_PER_RUN = 5  # cap catch-up work (e.g. imported adventures) per turn
MAX_EMBED_BATCH = 32
RETRIEVAL_WINDOW_TOKENS = 600  # recent story text used as the similarity query
RETRIEVAL_WINDOW_ACTIONS = 4  # ...taken from this many of the newest actions
SUMMARY_MAX_WORDS = 250

MEMORY_SYSTEM_PROMPT = (
    "You compress interactive-fiction story excerpts into memories. Respond with "
    "1-2 plain sentences in past tense stating the concrete facts and events "
    "(names, places, items, promises, injuries). No preamble, no commentary."
)
SUMMARY_SYSTEM_PROMPT = (
    "You maintain the running summary of an interactive-fiction story. Respond "
    "with only the updated summary: a single plain-prose overview of the plot "
    f"so far, at most {SUMMARY_MAX_WORDS} words. Preserve important established "
    "facts; compress older events harder than recent ones."
)

# Adventures with a post-turn task currently running (single-process app).
_running: set[int] = set()
# Strong refs to in-flight tasks — the event loop only keeps weak references,
# so a fire-and-forget task can otherwise be garbage-collected mid-run.
_tasks: set[asyncio.Task] = set()


# BYOK-only by construction: both factories below take the user's own
# endpoint/key straight from Settings and never auth.DEMO_*, so summarization
# and embedding can't spend the shared demo key (their call sites are also
# skipped when using_demo). Don't "fix" this by passing a ProviderConfig in —
# summary_model/embedding_model are free-form user input and are not on the
# demo whitelist.
def summary_provider(settings: models.Settings) -> OpenAICompatibleProvider:
    return OpenAICompatibleProvider(
        settings.endpoint_url,
        settings.api_key_plain,
        settings.summary_model or settings.model,
        settings.api_mode,
        settings.reasoning_max_tokens,
    )


def embedding_provider(settings: models.Settings) -> OpenAICompatibleProvider:
    return OpenAICompatibleProvider(
        settings.endpoint_url, settings.api_key_plain, settings.embedding_model
    )


def set_vector(memory: models.Memory, vector: list[float] | None) -> None:
    """Store (or clear) a memory's embedding.

    Every column that describes the vector moves together: `embedding_blob` is
    what the ranking reads and `embedded` is the flag everything else reads.
    Going through one function is what keeps them in step — and it is also the
    only place a stored vector can change, which is what makes the cache below
    safe to invalidate here and nowhere else.

    The one caller that legitimately cannot come through here is the bulk
    clear in `routers/settings.py` when the embedding model changes. It has to
    set the same two columns by hand; see the note there.
    """
    memory.embedding_blob = None if vector is None else vectors.pack(vector)
    memory.embedded = vector is not None
    cached = _vector_cache.get(memory.adventure_id)
    if cached is not None:
        cached.pop(memory.id, None)


# ---------- The vector cache ----------

# adventure id -> {memory id: vector}, most-recently-used last.
#
# Turns for one adventure arrive back to back, and the bank barely changes
# between them, so re-reading every vector each turn is the same 600 KB over
# and over. Vectors are held as array("f") — 4 bytes a component, the same
# 6 KB the column holds. A list of Python floats would be eight times that.
#
# Correctness rests on two things. Anything that *changes* a vector goes
# through set_vector, which drops that one entry. Anything that *removes* a
# memory from play — eviction, deletion, pruning, an edit clearing the vector —
# takes it out of the catalogue query below, and entries missing from the
# catalogue are dropped on the next read. So nothing has to remember to call an
# invalidate, which is the failure this design is chosen to avoid.
#
# In-process, so it assumes one worker. That is what the deploy runs; a second
# worker would each keep their own copy and both would still be correct on
# eviction and deletion, but a vector rewritten by one could go stale in the
# other until that memory next leaves the catalogue.
_vector_cache: OrderedDict[int, dict[int, array]] = OrderedDict()
VECTOR_CACHE_ADVENTURES = 8  # ~600 KB each at a 100-memory bank


def forget_cached_vectors(adventure_id: int) -> None:
    """Drop an adventure's cached vectors. Only needed when the adventure
    itself goes away — everything else self-corrects (see above)."""
    _vector_cache.pop(adventure_id, None)


def _vectors_for(db: Session, adventure_id: int, ids: list[int]) -> dict[int, array]:
    """The vectors for `ids`, reading only the ones not already held."""
    cached = _vector_cache.get(adventure_id)
    if cached is None:
        cached = _vector_cache[adventure_id] = {}
    _vector_cache.move_to_end(adventure_id)
    while len(_vector_cache) > VECTOR_CACHE_ADVENTURES:
        _vector_cache.popitem(last=False)

    wanted = set(ids)
    for gone in set(cached) - wanted:
        del cached[gone]
    missing = [memory_id for memory_id in ids if memory_id not in cached]
    if missing:
        rows = db.execute(
            select(models.Memory.id, models.Memory.embedding_blob)
            .where(models.Memory.id.in_(missing))
        ).all()
        for memory_id, blob in rows:
            if blob:
                cached[memory_id] = vectors.unpack(blob)
    return cached


def settled_count(adventure: models.Adventure) -> int:
    """How many story actions are old enough to summarize: all but the newest.

    See settled_story_actions for why one action is held back. Counting rather
    than listing keeps the post-turn pass off the whole story.
    """
    return max(history.count(adventure) - 1, 0)


def settled_slice(adventure: models.Adventure, start: int, length: int) -> list[models.Action]:
    """Settled story actions at positions [start, start + length).

    Callers must already have checked against `settled_count()`; this only
    fetches, it does not re-clamp.
    """
    return history.slice_(adventure, start, length)


def settled_story_actions(adventure: models.Adventure) -> list[models.Action]:
    """Story actions old enough to summarize: everything but the newest one.

    The plain-list form of the rule. The passes below use `settled_count` and
    `settled_slice` instead, which express the same thing without reading the
    whole story; this stays as the statement of what they must agree with.

    Only the *last* action can be retried, so once an action has another action
    after it, its text is final. Summarizing right up to the newest action meant
    a memory could describe an attempt the player then retried away — the
    memory's cursor has already advanced, so it is never regenerated, leaving a
    memory (and, downstream, a story summary) describing narration that is no
    longer in the story. Holding one action back costs a turn of latency and
    makes that unreachable.

    The result is always a prefix of story_actions(), so memory_cursor and
    summary_cursor stay valid positions and no action is ever skipped.
    """
    return story_actions(adventure)[:-1]


def _rewind_cursors_to_index(adventure: models.Adventure, index: int) -> None:
    """Move both cursors back to the position of Action.index `index`.

    The cursors are *positions* into story_actions() while Memory.source_* are
    Action.index values, so the two spaces have to be translated between (they
    diverge as soon as any action is deleted).
    """
    position = history.position_of_index(adventure, index)
    adventure.memory_cursor = min(adventure.memory_cursor, position)
    adventure.summary_cursor = min(adventure.summary_cursor, position)


def note_action_removed(adventure: models.Adventure, action: models.Action) -> None:
    """Keep the cursors pointing at the same actions when one is deleted from
    *before* them. Call BEFORE the delete, while the action is still in the list.

    memory_cursor counts actions from the start of the story, so removing an
    earlier action slides every later one down a slot — without this, an action
    that was never summarized shifts into the "already covered" range and is
    skipped forever.
    """
    if not history.is_story_text(action.text):
        return  # not in the list the cursors count, so nothing shifts
    # Actions are ordered by index, so "how many come before it" is exactly
    # "how many have a lower index" — no need to walk the list to find it.
    position = history.position_of_index(adventure, action.index)
    if position < adventure.memory_cursor:
        adventure.memory_cursor -= 1
    if position < adventure.summary_cursor:
        adventure.summary_cursor -= 1


def prune_dangling_memories(adventure: models.Adventure, db: Session) -> int:
    """Delete memories that summarized actions which no longer exist (e.g. after
    undo). source_start/source_end are Action.index values; a memory is dangling
    if any covered action is past the current end of the story. Returns the count
    removed.

    Throwing a memory away is not enough on its own: the actions it covered are
    still behind memory_cursor, so they would read as summarized with nothing
    describing them. Rewind to where the earliest discarded memory began, so
    those actions are summarized again.
    """
    max_index = history.max_action_index(adventure)
    dangling = [
        m for m in adventure.memories
        if m.source_end is not None and m.source_end > max_index
    ]
    if not dangling:
        return 0
    starts = [m.source_start for m in dangling if m.source_start is not None]
    for m in dangling:
        db.delete(m)
    if starts:
        _rewind_cursors_to_index(adventure, min(starts))
    return len(dangling)


# ---------- Retrieval (runs inside the turn, before build_context) ----------

async def retrieve_memories(
    adventure: models.Adventure,
    settings: models.Settings,
    *,
    update_stats: bool,
    exclude_action_id: int | None = None,
) -> dict | None:
    """Returns {"used": [{id, text, similarity, pinned}], "error": str|None},
    or None when the memory bank is off for this adventure. `update_stats`
    bumps use counters (real turns only, not Insights dry runs).
    `exclude_action_id` drops the action being retried from the similarity
    query, so the discarded attempt can't steer which memories come back."""
    if not adventure.memory_bank_enabled:
        return None
    if not settings.embedding_model.strip():
        return {"used": [], "error": "No embedding model configured in Settings."}
    db = object_session(adventure)
    if db is None:
        return {"used": [], "error": None}

    # Which memories are in play, and nothing else about them. This used to
    # walk adventure.memories, which loaded every row of the bank *including
    # its vector* — ~31 KB a memory, three megabytes a turn, 96% of everything
    # a turn read. Two ids and a flag per row is about eight bytes.
    catalogue = db.execute(
        select(models.Memory.id, models.Memory.pinned).where(
            models.Memory.adventure_id == adventure.id,
            models.Memory.forgotten.is_(False),
            models.Memory.embedded.is_(True),
        )
    ).all()
    if not catalogue:
        return {"used": [], "error": None}

    recent = history.tail(adventure, RETRIEVAL_WINDOW_ACTIONS, exclude_action_id)
    query = truncate_to_last_tokens(
        "\n\n".join(a.text for a in recent), RETRIEVAL_WINDOW_TOKENS
    )
    if not query.strip():
        return {"used": [], "error": None}

    try:
        [query_vec] = await embedding_provider(settings).embed([query])
    except ProviderError as exc:
        return {"used": [], "error": str(exc)}

    held = _vectors_for(db, adventure.id, [memory_id for memory_id, _ in catalogue])
    scored = sorted(
        (
            (cosine(query_vec, held[memory_id]), memory_id, pinned)
            for memory_id, pinned in catalogue
            if memory_id in held
        ),
        key=lambda row: row[0],
        reverse=True,
    )
    # Pinned memories are always used and count toward top_k, so the injected
    # set never exceeds the configured budget (unless pinned alone exceed it).
    top_k = max(1, settings.memory_top_k)
    used = [row for row in scored if row[2]]
    remaining = max(0, top_k - len(used))
    used += [row for row in scored if not row[2]][:remaining]
    used.sort(key=lambda row: row[0], reverse=True)
    if not used:
        return {"used": [], "error": None}

    # Only now, for at most top_k rows, is the text worth fetching.
    used_ids = [memory_id for _, memory_id, _ in used]
    texts = dict(
        db.execute(
            select(models.Memory.id, models.Memory.text)
            .where(models.Memory.id.in_(used_ids))
        ).all()
    )

    if update_stats:
        # synchronize_session=False: nothing in this request reads the counters
        # back, and matching the UPDATE against loaded objects would mean having
        # loaded them, which is the cost this whole path exists to avoid.
        db.execute(
            update(models.Memory)
            .where(models.Memory.id.in_(used_ids))
            .values(use_count=models.Memory.use_count + 1, last_used_at=models.utcnow())
            .execution_options(synchronize_session=False)
        )

    return {
        "used": [
            {"id": memory_id, "text": texts.get(memory_id, ""),
             "similarity": round(score, 4), "pinned": pinned}
            for score, memory_id, pinned in used
        ],
        "error": None,
    }


# ---------- Post-turn background work ----------

def schedule_post_turn(adventure: models.Adventure) -> None:
    """Fire-and-forget summarization/embedding work after a turn is saved."""
    if not (adventure.auto_summarize or adventure.memory_bank_enabled):
        return
    if adventure.id in _running:
        return
    task = asyncio.get_running_loop().create_task(run_post_turn(adventure.id))
    _tasks.add(task)
    task.add_done_callback(_tasks.discard)


async def run_post_turn(adventure_id: int) -> None:
    if adventure_id in _running:
        return
    _running.add(adventure_id)
    db = SessionLocal()
    try:
        adventure = db.get(models.Adventure, adventure_id)
        if adventure is None:
            return
        # Settings are per-user (Phase 8): use the adventure owner's row.
        settings = (
            db.query(models.Settings)
            .filter(models.Settings.user_id == adventure.user_id)
            .first()
        )
        if settings is None:
            return
        # Undo/retry can shrink the action list below a stored cursor, which
        # would stall summarization until the story grew past it again.
        # Deliberately the FULL count, not the settled one: an adventure that
        # was caught up under the old rule can have a cursor equal to the action
        # count, and clamping to settled would rewind it one step, re-covering
        # an already-summarized action in the next block. Both consumers below
        # read settled actions and bail on a negative remainder, so a cursor
        # briefly sitting one past the settled end is harmless.
        total = history.count(adventure)
        adventure.memory_cursor = min(adventure.memory_cursor, total)
        adventure.summary_cursor = min(adventure.summary_cursor, total)
        if adventure.auto_summarize:
            await _create_due_memories(adventure, settings, db)
            await _update_story_summary(adventure, settings, db)
        if adventure.memory_bank_enabled and settings.embedding_model.strip():
            await _embed_pending(adventure, settings, db)
        _evict_over_capacity(adventure, settings, db)
    finally:
        db.close()
        _running.discard(adventure_id)


async def _create_due_memories(
    adventure: models.Adventure, settings: models.Settings, db: Session
) -> None:
    provider = summary_provider(settings)
    for _ in range(MAX_MEMORIES_PER_RUN):
        # Re-counted each pass: a memory just committed doesn't change the
        # count, but this loop is the only thing that moves the cursor, so the
        # comparison has to be against a total that is still current.
        settled = settled_count(adventure)
        cursor = adventure.memory_cursor
        if settled < MEMORY_START or settled - cursor < MEMORY_INTERVAL:
            return
        block = settled_slice(adventure, cursor, MEMORY_INTERVAL)
        if len(block) < MEMORY_INTERVAL:
            return
        excerpt = truncate_to_last_tokens("\n\n".join(a.text for a in block), 2000)
        try:
            text = await provider.complete(
                MEMORY_SYSTEM_PROMPT, f"Story excerpt:\n\n{excerpt}\n\nMemory:"
            )
        except ProviderError:
            return  # logged in the debug page; cursor unchanged → retried next turn
        if not text:
            return
        memory = models.Memory(
            adventure_id=adventure.id,
            text=text,
            source_start=block[0].index,
            source_end=block[-1].index,
        )
        # Phase 14: hang it off the node it summarised, so a fork inherits the
        # memories of the path it forked from and nothing else.
        tree.place_memory(db, adventure, memory)
        db.add(memory)
        adventure.memory_cursor = cursor + MEMORY_INTERVAL
        db.commit()


async def _update_story_summary(
    adventure: models.Adventure, settings: models.Settings, db: Session
) -> None:
    settled = settled_count(adventure)
    if settled - adventure.summary_cursor < SUMMARY_INTERVAL:
        return

    # Fold in memories covering the uncovered stretch; fall back to raw story
    # text if memory creation is lagging (e.g. it just failed).
    # summary_cursor is a position into story_actions(); Memory.source_end is
    # an Action.index. Translate the cursor to an index boundary before
    # comparing — the two spaces diverge once actions are deleted or empty.
    if adventure.summary_cursor < settled:
        [first_uncovered] = settled_slice(adventure, adventure.summary_cursor, 1)
        boundary = first_uncovered.index
    else:
        last = settled_slice(adventure, settled - 1, 1) if settled else []
        boundary = last[0].index + 1 if last else 0
    new_events = [
        m.text
        for m in adventure.memories
        if m.source_end is not None and m.source_end >= boundary
    ]
    if new_events:
        events_text = "\n".join(f"- {t}" for t in new_events)
    else:
        block = settled_slice(
            adventure, adventure.summary_cursor, settled - adventure.summary_cursor
        )
        events_text = truncate_to_last_tokens("\n\n".join(a.text for a in block), 2000)

    current = adventure.story_summary.strip()
    user_prompt = (
        f"Current story summary:\n{current or '(none yet)'}\n\n"
        f"New events since the last update:\n{events_text}\n\n"
        "Updated summary:"
    )
    try:
        text = await summary_provider(settings).complete(
            SUMMARY_SYSTEM_PROMPT, user_prompt, max_tokens=600
        )
    except ProviderError:
        return
    if not text:
        return
    adventure.story_summary = text
    adventure.summary_cursor = settled
    db.commit()


async def _embed_pending(
    adventure: models.Adventure, settings: models.Settings, db: Session
) -> None:
    # A query, not a walk of adventure.memories: this ran every turn and pulled
    # the whole bank's vectors to find the handful that had none.
    pending = (
        db.query(models.Memory)
        .filter(
            models.Memory.adventure_id == adventure.id,
            models.Memory.embedded.is_(False),
            models.Memory.forgotten.is_(False),
        )
        .order_by(models.Memory.id)
        .limit(MAX_EMBED_BATCH)
        .all()
    )
    if not pending:
        return
    try:
        new = await embedding_provider(settings).embed([m.text for m in pending])
    except ProviderError:
        return
    for memory, vector in zip(pending, new):
        set_vector(memory, vector)
    db.commit()


def _evict_over_capacity(
    adventure: models.Adventure, settings: models.Settings, db: Session
) -> None:
    # Counting and ranking are both things the database does without sending
    # anything back. Walking adventure.memories to count them fetched every
    # vector in the bank, every turn, whether or not anything was over capacity.
    in_this_bank = (models.Memory.adventure_id == adventure.id,
                    models.Memory.forgotten.is_(False))
    active = db.execute(
        select(func.count(models.Memory.id)).where(*in_this_bank)
    ).scalar() or 0
    overflow = active - max(1, settings.memory_bank_capacity)
    if overflow <= 0:
        return
    doomed = db.execute(
        select(models.Memory.id)
        .where(*in_this_bank, models.Memory.pinned.is_(False))
        .order_by(
            models.Memory.use_count,
            func.coalesce(models.Memory.last_used_at, models.Memory.created_at),
        )
        .limit(overflow)
    ).scalars().all()
    if not doomed:
        return  # every active memory is pinned; capacity yields to the pins
    db.execute(
        update(models.Memory)
        .where(models.Memory.id.in_(doomed))
        .values(forgotten=True)
        .execution_options(synchronize_session=False)
    )
    db.commit()
    # The bulk UPDATE went around any loaded objects, so anything still holding
    # the collection would see the evicted memories as active.
    db.expire(adventure, ["memories"])