m7-imported-knowledge
5
Commits
| Author | SHA1 | Message | Date | |
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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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JesseMarkowitzandClaude Opus 5
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b7005e6fdd |
M5: genre-neutral authoritative narrative state, with review corrections
Replaces AI-DnD's RPG relative-delta world state with the genre-neutral typed
narrative state of ADR 010: explicit, absolute, allowlisted events proposed by
the model, validated by the application, applied to one authoritative document,
and snapshotted per position so restore stays a row read.
This commit includes the corrective pass that followed the independent review
in planning/reports/M5-IMPLEMENTATION-REPORT.md. The invariant it exists to
hold is:
visible active transcript position == stored head == authoritative state
Narrator editing (D10, STORY-BRANCH-SEMANTICS §§14-15)
A narrator edit no longer rewrites a row. It returns to the state before the
turn, takes the reader's exact text as the accepted narration, re-derives the
state that text implies, and becomes a new active continuation — while the
original narration keeps its words, its live flag and its whole future as
retained history. At the tip the correction is another take; with story below
it, it forks. No new history machinery: this is the existing fork/take/head
path with the reader's text in place of a generated reply. The §14A refusal
is therefore gone for narrator turns, and remains only for player input.
Pre-M5 positions
Migration 88 backfills the empty narrative document onto every action written
before M5, and a missing snapshot now restores the empty document instead of
leaving the previous position's state standing. Restoring to an old Save
Point no longer leaves a later position's entities and facts on screen.
Narrator context
Replayed history carries prose only; the machine-readable block is no longer
reconstructed into past turns, where it contradicted the authoritative state
in the same prompt. A fact withdrawn by a manual correction is now named as
no longer true, with the reader's reason, rather than silently dropped.
Also
- state_changes joins the action-list bulk read, removing one query per row.
- Extraction takes only the application's own protocol payload: an ordinary
```json or ```python block in a story survives, and a mangled proposal
still does not reach the reader.
Planning: ADR 013 records the authoritative document shape; §§14-15/14A, D10,
C04 and BUILD-MILESTONES are updated to describe what exists. Debt is recorded
against M8 (scenario editor UX) and M9 (export of the audit trail).
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01PWU4gTfLYY6Qq9U7aa9Qw2
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JesseMarkowitzandClaude Opus 5
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279a871a77 |
Planning: add the M4 implementation review report and rotate M3's
Reporting pass only. No application code, no test, and no product requirement changes. Result: PASS WITH CORRECTIVE WORK REQUIRED. M4's Definition of Done is met and demonstrated at the API level, including across a real two-process restart. The load-bearing constraint holds under inspection rather than assertion: the only head-field assignment M4 added anywhere in the backend is one line in head.py, and an exhaustive grep of the diff finds no second mechanism that forks, prunes memories, reconstructs state, filters the transcript or recomputes Redo. Three corrective items, all M4's own, none in the head model: - GET /checkpoints is an N+1 fetching whole Action rows including prose -- measured at 53 SELECTs for 25 Save Points against 4 for the branch panel, in a codebase that keeps test_egress.py for this exact class of mistake; - deleting a branch silently deletes Save Points naming it, and the branch panel's confirmation does not say so. M4 added the consequence to an existing destructive action without updating its warning; - the shipped D11/L03 tests restart a client, not a process, so the suite is weaker than the acceptance items it is named for. Both pass here only because the report re-ran them across a real process boundary by hand. The browser smoke test is NOT PERFORMED, for M4 and still for M3. Firefox is a snap that hangs past 90s on a trivial headless screenshot; there is no Xvfb, no display, no driver library. Two consecutive milestones now carry an unperformed browser requirement, which the report raises as a standing acceptance risk rather than a defect in either milestone's code. Evidence recorded: 680 backend tests pass (42 M4, 130 M3 invariants, 93 security/local-only), frontend lint and build clean, Docker build clean, migration 80 verified against a representative pre-M4 database with both cascades and zero possible orphans, and I04 verified through a real round trip with branch ids remapped 1->3 and 2->4. M4 is NOT accepted by this report, and M5 is NOT authorized. That decision belongs to whoever reviews this. Rotation: planning/reports/M3-IMPLEMENTATION-REPORT.md moves to planning/archive/milestone-reports/ as a pure rename, contents unedited (git reports 100% similarity, 0 insertions, 0 deletions). Six path references in five active documents are updated because the path changed and for no other reason -- no status claim, no wording change. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01PWU4gTfLYY6Qq9U7aa9Qw2 |
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JesseMarkowitzandClaude Opus 5
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d27ee34901 |
Docs: consolidate active planning and archive historical material
The planning package had grown to where a new agent could not tell what was authoritative. Phase 0 execution prompts sat beside the specification; four completed milestone reports sat beside the current one; and upstream AI-DnD's own `plan/` build log and `docs/` project site still described a hosted, scripted, multi-user product with accounts — every screenshot in it showed a Scripts tab and a Sign up button, none of which has existed since M2. `planning/archive/` now holds the history and says so in its own README: `phase0/` for the research that chose AI-DnD, `milestone-reports/` for M1 and M2, `decisions/` for ADR 008, the Phase-0-before-build gate Phase 0 satisfied. `planning/reports/` holds only the current milestone's report, because that is the one M4 planning has to read; it moves to the archive when M4's replaces it. Deleted rather than archived: the Phase 0B execution prompts and the handoff/status/summary documents, the Phase 0A discovery and triage reports, upstream's `plan/` and `docs/` trees, and `frontend/README.md`, which was Vite's template boilerplate. All of it is in Git history, and the two recommendation reports carry every conclusion the deleted research reached. Archived documents are kept verbatim. Paths written inside them point at where those files were when the document was written, which is the point: an evidence record that has been quietly edited is no longer evidence. Active documentation is corrected where it pointed at the removed trees or described removed capability as present. `DEVELOPMENT.md`'s "things M1 did not touch" list had gone stale at M2 and claimed QuickJS scripting was still tested; its test count was 604 against an actual 638. `README.md` loses the upstream CI badge, which reported upstream's pipeline rather than this fork's, and a reference to `backend/app/worldstate/engine.py`, a file that does not exist. `planning/README.md` is rewritten as the documentation index. New: `planning/PROJECT-SOURCES.md` and `planning/project-sources.txt`, the manifest of what belongs in the ChatGPT project's Sources. Source comments referring to the deleted trees are reworded; no behaviour changes. 638 backend tests pass, frontend lints and builds, and a reference scan over all 48 tracked Markdown files reports no unresolved path in active documentation. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01NCbwH7yLGKsj1rhXXzKSCu |