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Commits
| Author | SHA1 | Message | Date | |
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 JesseMarkowitzandClaude Opus 5
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44edece67e |
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 |
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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 |