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JesseMarkowitzandClaude Opus 5 480414efe0 M7: a first-class imported knowledge library
A campaign can import local .txt and .md files as Canon, Reference or
Inspiration, and the class is load-bearing rather than a label: it decides the
words a passage is framed with in the prompt, the weight it carries when
passages are ranked, and which budget it competes in when the context is tight.

This is a separate subsystem, which is the Phase 0B decision
(IMPORTED-KNOWLEDGE-DESIGN.md §73). Story Cards do not carry classification,
provenance, content identity, chunking, an index or a lifecycle, and they were
not promoted into something that does. Nothing here reads or writes one.

The subsystem, in backend/app/knowledge/:

  classes      the three classes, their weights, and the prompt framing
  chunking     deterministic, heading-aware, 60-800 tokens, no overlap
  fts          SQLite FTS5 with porter stemming; scoped and bounded in SQL
  importer     validate, hash, store, chunk, index — in one transaction
  embeddings   local Ollama vectors through the shared provider
  retrieval    query construction, hybrid merge, rerank
  inject       the budgeted cut and the rendered prompt sections

Relevance admission is a separate stage from ranking, and that separation is
the milestone's most expensive lesson. An independent review found the first
implementation deciding relevance with a floor expressed as a share of the best
candidate — which the best clears by construction — so a passage was admitted on
every turn regardless of the scene. A query about tide tables and container
tonnage retrieved all five sources of a fantasy campaign, narrator-only hidden
Canon among them.

So the pipeline is now:

  candidate generation -> admission -> ranking -> class weighting -> budget

Admission reads raw, candidate-set-independent signals: the cosine the model
returned, and how many distinct meaningful query terms a passage contains.
Ranking reads normalized ones, because bm25 has no fixed range and cosine's zero
is not zero. Normalization decides order among things that matched; it can never
decide whether anything matched. Authority is applied after admission, so a
class orders what matched and never rescues what did not.

Retrieval may therefore return nothing, and on a scene unrelated to the library
it does.

The other decisions that each replaced an obvious wrong one:

- The class multiplies relevance rather than adding to it. An additive bonus
  satisfies "Canon outranks Reference" and makes "do not include irrelevant
  Canon" impossible, because a large enough constant wins on its own.
- The semantic floor is measured, not guessed: 113 production-path pairs against
  nomic-embed-text put targeted matches at 0.55-0.85 and off-topic pairs at
  0.36-0.56, and 0.58 sits between them. Because it is a property of that model
  and not of cosine similarity, it is keyed to the model rather than applied to
  whatever is configured: an embedding model with no measured calibration in
  this build does not borrow the number. Semantic admission is skipped, the
  campaign retrieves lexically, and the reason is stated in the knowledge status
  and in the turn's provenance. Degrading to lexical keeps the library usable;
  lending the threshold to an unmeasured model is how the admitted-everything
  defect would return.
- One lexical term is not evidence. Two distinct meaningful terms, or one that
  is neither a standing campaign entity nor a negligible share of the query.
  The stop list grew from 42 words to 261, all function words — no subject
  matter, because a stop list that removes subject matter stops finding "The
  Silver Key".
- Lexical retrieval is a production path, not a fallback. It finds the proper
  nouns and invented terms a setting bible is made of, and the library is fully
  usable with no embedding model configured.

Safety is structural rather than filtered. Imported text reaches the prompt
whole, inside a section that says what it is, under a rule stating the authority
order in words and refusing every instruction inside it. No endpoint accepts a
filesystem path, so H08 has no mechanism to escape from. Nothing renders
imported content as HTML, so a script tag is five visible characters and a
remote image is never fetched. Import, chunking, indexing, retrieval and a turn
open no socket at all; only embeddings do, through the endpoint allowlist the
memory bank already uses.

Provenance is the rendered text, not a foreign key: deleting a source cannot
turn a historical turn's evidence into dangling ids.

Schema: knowledge_sources, knowledge_chunks, knowledge_embeddings, and an FTS5
virtual table attached to knowledge_chunks as a DDL hook so it is created and
dropped with the table it indexes. Migration 92. A pre-M7 database opens
unchanged and needs no sources to play.

Bundle: the source content and the reader's judgements about it travel; the
passages, index rows and vectors are rebuilt on import, so a restored campaign
is searchable immediately without a reindex step.

One runtime dependency: python-multipart, Starlette's multipart parser. It is
what makes the upload surface possible, and the upload surface is why no
pathname is ever accepted.

The test doubles were the reason the defect shipped, so they were corrected too.
The retrieval stub scored unrelated text at 0.06-0.20 where the real model
scores it at 0.43-0.44, and its docstring said it had deliberately removed the
constant component that "would put a similarity floor under every pair" — which
is exactly the property real models have. The stub now has that floor, one test
fails if it is ever removed, and another reproduces the superseded rule and
asserts it is still fooled by the same fixture. Run against the pre-corrective
implementation, the new suite fails 13 of 18.

Tests: 939 passed, 14 skipped (836/7 at M6). 110 new across seven files, one of
which mocks nothing between itself and Ollama and re-measures the similarity
separation on every run. 43/43 checks in a real Firefox, reproduced.
Docker build clean.

Four other defects found by review or by the browser run were fixed here rather
than carried: an unreachable relevance constant that appeared to enforce
something and did not; acceptance tests using the wrong fixture files, so G07's
trap was never exercised; a bidirectional override surviving into displayed
filenames; and, from the implementation pass, the Insights panel showing M5's
two state sections as raw keys and the source inspector refetching on every
keystroke.

M7 was independently reviewed, which returned PASS WITH CORRECTIVE WORK
REQUIRED. Both blocking findings are closed, and closeout resolved the
embedding-model calibration boundary the corrective pass had left as debt.
planning/reports/M7-IMPLEMENTATION-REPORT.md carries the review, the corrective
closeout and the closeout verification in sequence, none overwriting another.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017HdaXiFbscatQaLS7dJk6b
2026-09-06 15:40:13 -04:00

421 lines
18 KiB
Python

"""Playing a turn: the model call, the SSE stream, and the one-turn-at-a-time lock.
Everything a test needs to intercept lives here, and other modules reach it as
`turns.<name>` rather than importing it by value. That matters twice. The turn
lock guards one set only while one module owns it. And a test that replaces
`OpenAICompatibleProvider` or `generate_turn` patches this module, which every
caller reads through.
"""
import threading
from fastapi import Depends, HTTPException, Request
from fastapi.responses import StreamingResponse
from sqlalchemy.orm import Session
from ... import (
attempts, head, limits, memorybank, models, narrative, schemas, tree,
worldstate,
)
from ...context import ContextOverflow, build_context, cursors
from ...knowledge import retrieval as knowledge_retrieval
from ...database import get_db
from ...providers import OpenAICompatibleProvider, PromptParts, ProviderError
from ...sse import SSE_HEADERS, sse, turn_error
from ..settings import get_settings
from .deps import CurrentUser, current_adventure, router
from .nodes import _move_to_after, next_depth
from .paging import annotate_takes
def world_delta_of(snapshot: dict | None) -> dict | None:
"""Returns the bulk-read slice of a context snapshot, for `Action.world_delta`.
`context_snapshot` is deferred because it holds the whole assembled prompt.
The parts that every action needs get their own small column instead: the
world-change chips, the emit block replayed into history, and the refusal
note fed back to the model. Update this function wherever a snapshot is
written.
Carry all three report lists, not just `applied`. `Action.world_changes`
marks a chip from `clamped` and builds its refusal chips from `rejected`,
and `worldstate.refusals` reads both. Storing `applied` alone left every
consumer unable to tell a refused change from one that worked, which is the
distinction this column exists to carry. The two extra lists are subsets of
one turn's block, so they cost a few hundred bytes per action at most.
"""
ws = (snapshot or {}).get("world_state")
if not isinstance(ws, dict):
return None
report = ws.get("report") or {}
return {
"delta": ws.get("delta") or {},
"applied": report.get("applied") or [],
"clamped": report.get("clamped") or [],
"rejected": report.get("rejected") or [],
}
# One turn at a time per adventure. The set lives in memory, which is enough for
# a single-process local app. Sync endpoints run in a threadpool, so the
# check-and-add needs a lock. The check also has to run during the request
# rather than when the SSE generator first runs. Otherwise two rapid requests
# both pass the check and generate concurrently.
_active_turns: set[int] = set()
_active_turns_guard = threading.Lock()
def acquire_turn_lock(adventure_id: int):
"""Claims the adventure's turn slot atomically. `with_turn_lock` releases it."""
with _active_turns_guard:
if adventure_id in _active_turns:
raise HTTPException(409, "A turn is already generating for this adventure.")
_active_turns.add(adventure_id)
async def with_turn_lock(adventure_id: int, gen):
"""Wraps an SSE generator so that it releases the `acquire_turn_lock` lock."""
try:
async for event in gen:
yield event
finally:
_active_turns.discard(adventure_id)
def format_player_input(action_type: str, text: str) -> str:
"""Formats player input the way AI Dungeon does."""
text = text.strip()
if action_type == "say":
text = text.strip('"')
if text and text[-1] not in ".!?…":
text += "."
return f'> You say "{text}"'
if action_type == "do":
if text.lower().startswith("you "):
text = text[4:]
if text and text[-1] not in ".!?…":
text += "."
return f"> You {text}"
return text # The "story" type is appended as raw text.
def action_json(action: models.Action, db: Session | None = None) -> dict:
"""Serializes one action for the wire.
Passing `db` fills in the pager numbers, and a turn that was just played must
pass it. The attempt that turn created is often the second one at its
coordinate, so the message needs a pager that the client cannot infer from a
count of one. Without `db`, a retry showed no pager until the page reloaded.
The adventure GET has the same requirement and builds its window a third way.
"""
if db is not None:
annotate_takes(db, action.adventure_id, [action])
return schemas.ActionOut.model_validate(action).model_dump(mode="json")
async def generate_turn(
adventure: models.Adventure,
db: Session,
user: models.User,
retry_of: models.Action | None = None,
):
"""Streams the AI continuation as SSE, then stores the result.
If `retry_of` is set, the result is stored as a sibling of that AI action, at
the same turn and the same coordinate, and the discarded attempt stays where
it was written. Before calling, the caller must roll the adventure back to
the state before that turn. See `retry_action`. If this generator ends
without saving, the rollback is undone, so the state cannot diverge from the
text on screen.
"""
saved = False
try:
async for event in _generate_turn(adventure, db, user, retry_of):
if event is _SAVED:
saved = True
continue
yield event
finally:
if retry_of is not None and not saved:
# The turn failed with a provider error, an empty reply, or a
# disconnected client. No sibling was written, so the
# attempt on screen is still the live one. Restore the state it
# produced.
attempts.restore_state(adventure, retry_of)
db.commit()
# `_generate_turn` yields this sentinel once the action is committed. It tells
# the wrapper above to leave the rollback in place rather than reverse it.
_SAVED = object()
async def _generate_turn(
adventure: models.Adventure,
db: Session,
user: models.User,
retry_of: models.Action | None = None,
):
settings = get_settings(db, user)
# On a retry, the attempt being replaced is still the live node of its turn,
# because it stays live until a replacement exists. Filter it out of the
# context. Otherwise the model reads the attempt it is replacing as
# established story and writes a sequel to it.
replacing_id = retry_of.id if retry_of is not None else None
memories = await memorybank.retrieve_memories(
adventure, settings, update_stats=True, exclude_action_id=replacing_id
)
# M7: the imported library, retrieved for the position being read. Excluding
# the attempt being replaced matters here for the same reason it does for
# memories — the query is built from the recent story, and a discarded
# attempt must not steer which passages the replacement is given.
knowledge = await knowledge_retrieval.retrieve(
adventure, settings, exclude_action_id=replacing_id
)
try:
system_text, story_text, snapshot = build_context(
adventure,
settings,
memories,
exclude_action_id=replacing_id,
knowledge=knowledge,
)
except ContextOverflow as exc:
# M6: the protected context does not fit in the configured budget, so
# there is no prompt to send. This is a settings problem the reader can
# fix, and the message says how — reporting it as a failed turn keeps
# the story intact and tells them what to change, where building the
# prompt anyway would return a silently truncated reply.
yield turn_error(str(exc))
return
parts = PromptParts(system=system_text, story=story_text)
provider = OpenAICompatibleProvider(
settings.endpoint_url, settings.model, settings.api_mode,
settings.model_timeout_seconds,
)
chunks: list[str] = []
reasoning_chunks: list[str] = []
try:
async for kind, chunk in provider.generate(
parts, temperature=settings.temperature, max_tokens=settings.max_output_tokens
):
if kind == "reasoning":
reasoning_chunks.append(chunk)
yield sse({"type": "reasoning", "text": chunk})
else:
chunks.append(chunk)
yield sse({"type": "chunk", "text": chunk})
except ProviderError as exc:
yield turn_error(str(exc))
return
text = "".join(chunks).strip()
# The model's literal reply, kept for the Insights "Raw AI output" view. It
# still contains the world-state block, which the code below strips.
raw_output = text
if not text:
# The model streamed reasoning but no story text, so it spent its whole
# budget on reasoning. Report that rather than "empty response".
if reasoning_chunks:
detail = (
"The model used its entire token budget on reasoning and returned no "
'story text. Raise "Max output tokens" in Settings, set a "Reasoning '
'max tokens" cap, or switch to a non-reasoning model.'
)
else:
detail = "The AI returned an empty response."
yield turn_error(detail)
return
# M5: read the typed state proposal out of the reply, validate it, apply
# what survives, and strip the block from the displayed text.
#
# This replaced the Phase 12 relative-delta pipeline. The shape of the turn
# is unchanged — extract, referee, snapshot — because ADR 010 changed the
# protocol, not the lifecycle. What changed is that the referee now works on
# explicit typed events with absolute values, so an accepted proposal cannot
# mean something other than it says.
#
# A retry re-runs the same turn, so it is played at that turn's depth. This
# was `retry_of.index`, which held the same number until SP4. Depth stays
# correct once a branch has its own numbering.
ai_depth = retry_of.depth if retry_of is not None else next_depth(adventure)
text, parsed, raw_block = narrative.extract.split(text)
if not text.strip():
yield turn_error("The AI returned only a state update and no story text.")
return
review = narrative.validate.review(
parsed if parsed is not None else {"events": []},
narrative.store.current(adventure),
narrative.store.canon_of(adventure),
)
# Held until the action exists, because a proposal record names the node
# whose narration produced it and the node has no id yet. Everything lands
# in the single commit below (L01).
# The coordinate is read off the node after it is placed, not guessed here:
# `tree.place_action` assigns the branch, and a retry inherits the branch of
# the attempt it replaces.
pending_state = {
"review": review,
"parsed": parsed,
"raw_block": raw_block,
"unparseable": parsed is None and bool(raw_block),
}
snapshot["narrative_state"] = {
"accepted": review.accepted,
"rejected": [r.as_dict() for r in review.rejected],
"status": review.status,
}
snapshot["raw_output"] = raw_output
# The cost the endpoint reports for the call, including how much of the
# prompt came from cache rather than being billed in full. This is recorded
# per attempt, next to the prompt it priced.
snapshot["usage"] = provider.last_usage
reasoning = "".join(reasoning_chunks).strip() or None
ai_action = models.Action(
adventure_id=adventure.id,
depth=ai_depth,
type="ai",
text=text,
reasoning=reasoning,
context_snapshot=snapshot,
world_delta=world_delta_of(snapshot),
)
if retry_of is not None:
attempts.add_attempt(db, adventure, retry_of, ai_action)
db.add(ai_action)
# The text at this coordinate changed, so anything derived from it no
# longer describes the story. Withdraw the memory attached to the node
# and return that stretch to both passes. Before SP4 this code was
# unreachable, because the summarizer held the newest action back until
# a turn landed on top of it. `memorybank.SETTLE_SLACK` keeps a memory
# off the tip again, for cost rather than for correctness, so this is
# now the rare case: undo or delete can carry a summarized node back to
# the tip, and then a retry of it lands here. See `memorybank`.
memorybank.forget_node(db, adventure, retry_of)
cursors.rewind_all(adventure, retry_of.branch_id, ai_depth - 1)
# Flush so the new attempt has an id. The session does not autoflush,
# and attempts page in id order, so a read taken before this point puts
# the newest attempt nowhere.
db.flush()
else:
tree.place_action(db, adventure, ai_action)
db.add(ai_action)
db.flush()
# The state lands after the node exists and before the one commit, so the
# narration, the head, the accepted events, the provenance and the snapshot
# are one transaction. L01 forbids any window in which a turn looks accepted
# while its state is half-written, and the cheapest guarantee is to have a
# single commit rather than two that could get out of step.
new_state, _proposal = narrative.store.record(
db, adventure,
review=pending_state["review"],
raw_block=pending_state["raw_block"],
parsed=pending_state["parsed"],
action=ai_action,
branch_id=ai_action.branch_id,
depth=ai_action.depth,
model_name=settings.model or "",
source="accepted_story",
)
if pending_state["unparseable"]:
_proposal.status = "unparseable"
before_state = narrative.store.current(adventure)
narrative.store.set_current(adventure, new_state)
ai_action.state_changes = {
"accepted": pending_state["review"].accepted,
"rejected": [r.as_dict() for r in pending_state["review"].rejected],
"summary": narrative.apply.diff(before_state, new_state),
}
attempts.snapshot_outcome(adventure, ai_action)
adventure.updated_at = models.utcnow()
db.commit()
db.refresh(ai_action)
yield _SAVED
yield sse({"type": "done", "action": action_json(ai_action, db)})
# Phase 6: schedule summarization and embedding without waiting for them.
# The task opens its own database session.
memorybank.schedule_post_turn(adventure)
async def run_player_turn(
adventure: models.Adventure,
db: Session,
payload: schemas.ActionCreate,
user: models.User,
preformatted: bool = False,
):
"""Plays a player's turn: their action, then the reply to it.
`preformatted` means the text already carries the `> You ...` conventions and
is written as-is. That applies when the player retakes a turn they already
played (SP9). The editor is seeded with the stored text, which is already
formatted, and a plain edit puts that same text in the box and writes it back
verbatim. Formatting it a second time produces `> You > You ...`.
"""
# An empty do, say, or story action behaves as a continue.
if payload.type != "continue" and payload.text.strip():
formatted = (
payload.text.strip() if preformatted
else format_player_input(payload.type, payload.text)
)
player_action = models.Action(
adventure_id=adventure.id,
depth=next_depth(adventure),
type=payload.type,
text=formatted,
)
# The state this node leaves behind. The AI turn after it starts here,
# and a retry of that turn rolls back to here.
attempts.snapshot_outcome(adventure, player_action)
tree.place_action(db, adventure, player_action)
db.add(player_action)
db.commit()
db.refresh(player_action)
# The new action was added through its foreign key, so the loaded
# `adventure.actions` collection is stale. Without this expire,
# `build_context` for the AI action does not see the player action
# that was just saved.
db.expire(adventure, ["actions"])
yield sse({"type": "player", "action": action_json(player_action, db)})
async for event in generate_turn(adventure, db, user):
yield event
@router.post("/{adventure_id}/actions")
def create_action(
adventure_id: int,
payload: schemas.ActionCreate,
request: Request,
db: Session = Depends(get_db),
user: models.User = CurrentUser,
adventure: models.Adventure = Depends(current_adventure),
):
limits.check_row_cap("actions", db, user, adventure=adventure)
acquire_turn_lock(adventure_id)
try:
_move_to_after(db, adventure, payload.after_id)
# The first write below a moved-back head is where a divergence happens
# (M3). Undo alone does not fork — the user may be reading, or about to
# Redo — so this is the moment the story states which continuation it
# means. The displaced future keeps its rows on the branch being left.
# A head already at the tip, which is every ordinary turn, forks nothing.
if head.fork_if_behind_head(db, adventure):
db.commit()
db.refresh(adventure)
except BaseException:
_active_turns.discard(adventure_id)
raise
return StreamingResponse(
with_turn_lock(adventure_id, run_player_turn(adventure, db, payload, user)),
media_type="text/event-stream",
headers=SSE_HEADERS,
)