144406cd481df567c4d96131002d907785045e9e
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Commits
| Author | SHA1 | Message | Date | |
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 JesseMarkowitzandClaude Opus 5
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144406cd48 |
M11: what the server will actually read
The release-validation milestone, and the thing it had to settle first was whether any of the earlier evidence meant what it said. M8 measured a deployment enforcing a 4,096-token input window while the application budgeted 16,384. Every request returned 200. What Ollama does with the excess is drop the oldest tokens, and the oldest tokens here are the system block — the narrator's rules and the campaign canon. A hundred-turn certification against that server would have looked perfect and proved nothing, which is why this milestone could not begin with a hundred turns. So the application asks now. Ollama's window is a property of how a model was loaded rather than of the request — sending num_ctx is accepted, ignored, and worse, reloads the model at the server's own default — so the only honest move is to find out and then tell the truth about it. /api/ps reports what a resident model is being served with, /api/show what an unloaded one will load with, both on the same host inference already uses, through the same endpoint policy and the same TLS trust store. A verified window is a ceiling on the budget; an unverified one leaves the budget alone and is recorded as unverified in the turn's own provenance, so an old turn can be asked afterwards whether it was built against a checked window. There is no third behaviour, and in particular no hard-coded 4,096: a number the server did not say would be right on one machine and wrong on the next. The proof that this is doing something is a campaign whose canon sits at the front of the prompt, 120 turns of history, and a 4,096-token window. The canon is still there afterwards and the oldest history is gone. The same campaign built the old way produces a prompt more than twice the window — the defect, reproduced, so the fix is measured against it rather than asserted. Two defects the validation found on its own, and they are the same defect twice: something was true and nobody was told. A manual state correction of four changes with one bad reference applied three, returned 201, and said nothing — while recording the refusal on the audit row nobody reads. It came to light because the identity diagnostic's own fixture was refused that way and the whole run proceeded on a campaign with no scene, which would have read as a model failure. And the narration-length setting moved no number: brief, medium and long each became one English sentence, while the numeric hint the model actually reads was derived from the global reply cap and said the same thing for all three. Both now say what they did. The other two post-M8 findings are closed as well. The tab said AI D&D, which no document had ever claimed it did not; it says Interactive Story now, with the open campaign first, and the name is the owner's decision rather than a find-and-replace to something narrower than the engine. After an Undo the reader could not tell where they had landed; the control row now ends with "Moment 11 · later story ahead", from the server's own answer, in the word the transcript already uses, with none of head, branch or depth anywhere near it. The identity diagnostic exists and the root cause does not. That campaign was destroyed, so no cause can be established — what M11 owes the finding is something that can classify the next occurrence, and a diagnostic that makes only the judgements a program can honestly make: duplicate keys, shared names, protagonist drift, state and context disagreeing. Whether prose misattributed a line is left to a person reading it beside its prompt, because a regex cannot read dialogue and one that pretended to would produce exactly the confident wrong answer this finding is about. Its detectors are proved to fire against a planted second Alice. Two entities may still share a display name. That was checked first, as the finding asked, and left permitted: a mother and a daughter, or a stranger giving a false name, are ordinary fiction, and refusing them to guard against a model mistake would refuse the wrong thing. What was missing was that it happened silently. It is reported now. Evidence, not inference: a hundred accepted turns against a real narrator with genuine process restarts; a real browser against the built SPA; a container with no network at all; a campaign moved into a data directory that never existed. Each was discarded and re-run whenever the product changed under it, and the runs that were thrown away are listed in the report with the reason, along with ten defects in the harnesses themselves — because a harness that has only ever agreed with itself is not evidence, and two of M8's five harness defects were masking real ones. No dependency was added, removed or upgraded. No acceptance test was retired, relaxed or reclassified. M11 is implemented and verified; it is not accepted, and there is no release tag. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Qyn3oRd4D6pi72nKBG725B |
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JesseMarkowitzandClaude Opus 5
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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 |
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JesseMarkowitzandClaude Opus 5
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a6e9c7a32b |
M6: branch-safe context, summaries and long-term story memory
Aligns the inherited AI-DnD memory and context foundation with the history,
authority and state model M3-M5 established. Long stories now reach the narrator
through a bounded, lineage-safe, inspectable context rather than a growing
transcript.
This commit includes the corrective work that followed the independent review in
planning/reports/M6-IMPLEMENTATION-REPORT.md. The first implementation reported
E03 as passing and it was not; the report records that history rather than
hiding it.
What was already correct, and was kept rather than rebuilt
Memory lineage. Memories already carried (branch_id, depth) and retrieval
already filtered through the capped-path clause; the ten-step negative control
was measured passing against
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JesseMarkowitz
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8c65ae99de |
M2: cut the hosted product away from the local one
94 files, +1,395 -6,578. Three files are new; twenty-four are gone. The milestone is subtraction, and what is left is the single-user local storyteller the specification describes. Removed in full: campaign scripting and its QuickJS sandbox; multi-user accounts, guest sessions, login, registration and the shared demo key; the visitor-analytics tables, dashboard and page beacon; the access log of sign-ins, addresses and devices; per-IP and per-user rate limiting and quotas; Render deployment config; Postgres and psycopg; cloud inference providers, the API-key field and the key encryption that existed to store it; session-cookie signing. None of it was hidden behind a flag — the routes are gone and answer 404. Two things were kept that the brief allowed keeping. The `users` table and its foreign keys stay as an internal ownership detail, because rewriting them out means a migration across most of the schema to delete a column that costs nothing; nothing creates a second user and no request carries an identity. Five inert tables and four inert columns stay for the same reason, so an M1 campaign database opens unchanged. The one addition is app/endpoints.py, which decides where a story may be sent. Loopback, RFC1918, link-local, unique-local and CGNAT — an explicit allowlist of networks, not a guess at what `ipaddress` means by "private", which calls the documentation ranges private and IPv6 loopback reserved. Every address a hostname resolves to must be in it, so a split answer does not squeak through, and the rule runs both when the endpoint is saved and before every outbound request, because a name that resolved to the LAN this morning can resolve elsewhere this afternoon. Known cloud hosts are named in the refusal so the error says why rather than looking like broken DNS. TLS is never traded against it: M1's shared trust context is intact on all four clients and there is no way to skip verification. The hardcoded 120-second model timeout is now a setting. That was not theoretical — on this GPU-less four-core host a cold load of qwen2.5:3b-instruct took 648.9 seconds to produce the first turn, while turns 2 to 5 of the same campaign took 3.6 to 13.1. Connect stays short at 10s so a wrong address still fails fast; the read timeout defaults to 300s and is bounded at 3600, because "wait longer" must stay a number. Two defects found while testing and fixed here. An unknown /api path fell through the SPA catch-all and came back as HTML with status 200, so a client asking for JSON parsed a web page instead of learning the route was gone. And AIDND_CORS_ORIGINS accepted "*", which on an unauthenticated loopback API would hand every page on the Internet a write handle on the campaign database; it now refuses to start. Verified rather than assumed. Offline, on a network with no route out and no DNS: five turns, retry with both takes retained, restart with an identical transcript digest, a failed model call leaving the accepted AI-turn count untouched, and a capture with zero non-loopback unicast packets. Against a real second machine on the LAN over HTTPS with a private CA: four turns, restart, and a capture showing 289 packets to the approved host, 344 loopback, zero anywhere else, zero DNS queries. Cloud and public endpoints refused with their reasons; no API key settable; every removed route 404. 604 backend tests pass, down from 648 by the fifteen retired with the subsystems they tested and up by the twenty-nine added for the endpoint policy and the removed surface. The scripting tests were not deleted: eight files used a JavaScript counter as instrumentation for the state snapshot and rollback machinery, which M2 does not touch, so the counter moved to the world-state engine and those tests still assert what they always did. Frontend lint and build are clean; the image builds, and its wheel-building stage is gone with quickjs. No M3 work. Undo is still destructive and there is still no Redo. |
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parththakkar106andClaude Opus 5
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2c57b1ceab |
Remove three kinds of duplication in the backend
Stage 2, items 1, 4, and 5 of `plan/17-refactor.md`. **One path resolver in `worldstate`.** `apply_delta` and `apply_override` routed `flags.<name>`, `milestones.<id>`, `world.<stat>`, `player.<stat>`, and `npc.<id>.<stat>` with parallel code, about 100 lines each. `_resolve` now says what a path points at and returns either a target or the rejection to report. Each function keeps its own write rule, because the rules genuinely differ: an override sets a number rather than adding to it, ignores `cooldown`, `max_delta_per_turn`, and the rule that a counter only counts up, and can un-set a milestone. A differential check ran both implementations over 3960 payloads: twenty paths, fourteen values, three starting states, plus every three-path combination. The results are identical except that 674 rejections from `apply_override` now carry a `fix` string. `apply_delta` already worded those, and the world-state editor renders them, so an override that names an unknown flag now explains itself the way a delta does. **`sse`, `SSE_HEADERS`, and `turn_error` move to `app/sse.py`.** Two routers stream, and `chat.py` had to import from `routers.adventures` to reach them. **`get_adventure_or_404` becomes the `current_adventure` dependency.** All 32 handlers repeated the call as their first statement. The ownership check now reads in the signature and runs before the body. FastAPI caches a dependency for one request, so the handler's `db` is the session the adventure came from. The generated OpenAPI document is byte-identical except on `rename_branch`, where `branch_id` is now listed before `adventure_id`, because that handler no longer names `adventure_id` itself. Parameter order in the document is cosmetic. Six tests in `test_state_revert.py` call `undo_turn` and `retry_action` directly rather than over HTTP. They pass the adventure they already hold instead of an id. 549 tests pass. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_014Dix4oGV3njgWRdu7P9t6r |
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parththakkar106andClaude Opus 5
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2fa812c056 |
Split the adventures router into a package
`backend/app/routers/adventures.py` held 2353 lines and 35 endpoints. It is now a package of 14 modules, the largest 443 lines. The split moves text rather than rewriting it. An AST comparison against the old file confirms all 86 definitions are identical, and the OpenAPI schema still lists the same 35 operations. Names a test replaces now live in `turns.py` only, and other modules reach them as `turns.<name>`. Rebinding a re-exported alias changes the alias and leaves every caller reading the original, so the package root does not re-export them. A patch aimed at the old target raises `AttributeError` instead of passing while doing nothing. Tests and the fixtures in `backend/tools/` say `adventures.turns.<name>`. The same rule keeps the turn lock working. One module owns `_active_turns`, so one lock guards one set. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_014Dix4oGV3njgWRdu7P9t6r |