feat(v3.9.0): suggest-links - link discovery with human review guard - #5
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feat(v3.9.0): suggest-links - link discovery with human review guard#5nicoechaniz wants to merge 5 commits into
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…te_chapter - memoryctl: update_chapter (in-place edit, FTS5 delete+insert, drops stale embeddings on content/title change, book title/slug sync with collision guard) and delete_chapter (FK cascade for embeddings/links, FTS row removal, empty-book pruning, raw_sha256 report for archival) - CLI: memoryctl.py update / delete subcommands - hmk-memory plugin: librarian tool gains update + delete actions (1.1.0) - tests: 11 new tests against a real temp library.db; replace stale test_get_tool_schemas_empty with an enum-invariant test - also commits the previously deployed-but-uncommitted librarian tool (v3.8.0) and integrates origin's v3.7.3 memoryctl path-resolution fix
- plugin cli.py: update/delete subcommands matching the librarian tool and memoryctl semantics (no-field update exits 2, --keep-book flag) - plugin README: document the librarian tool action table and the embedding-drop / raw_sha256 contracts
Consistent with upsert_book() bumping it on every add_text; covered by test_update_bumps_book_updated_at with a pinned now_ts.
- scripts/corpus_policy.py: file-level blocking (never-touch names/globs) + content-level secret scan (private keys, API tokens, JWTs, Bearer) - Selective embedding: code (.py/.js/.sh) and config (.yaml/.json/.toml) files get embed_disabled=1 at ingest time — FTS5-only, no cloud API - chapters.embed_disabled + embed_disable_reason columns (auto-migrated) - embedding_candidates() / embed-backfill skip disabled chapters - stats() reports embed_disabled breakdown by reason - add_file blocks protected files; ingest_any enforces corpus policy - update_chapter re-scans content for secrets - 30 new tests in tests/test_corpus_policy.py (83 total, all pass) Cards: t_116003b9 (P1 corpus policy) + t_c2f96fa4 (P1 selective embedding)
- New link_suggestions table (status candidate/accepted/rejected) - suggest_links(): K nearest neighbors via cosine similarity on embeddings Filters: no self-links, already-linked, same-book - list_link_suggestions(): list with both chapters' context - review_link_suggestion(): accept → creates chapter_links edge, reject → marks blocked for re-proposal - CLI: suggest-links, review-links (accept/reject/list) - Librarian tool: suggest_links action (read-only — guard against self-approval) - stats() reports suggestions + suggestions_total counts Card: t_9cc06fde (P2)
This was referenced Jul 29, 2026
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Aug 18, 2026
fix(cli): accept status dispatcher arguments
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Summary
Implements card t_9cc06fde (P2): link suggestion discovery via vector cosine similarity with mandatory human review.
Design
memoryctl suggest-linkscomputes K nearest vector neighbors from stored embeddings per chapter, filters out self-links, already-linked pairs (either direction), and same-book chapters. Stores proposals in newlink_suggestionstable with statuscandidate.memoryctl review-links --accept IDcreates realchapter_linksedges (link_type=suggested, weight=score).--reject IDblocks re-proposal permanently. The librarian tool exposessuggest_linksaction as read-only — accept/reject is CLI-side only, so the agent cannot self-approve graph mutations.Schema
link_suggestionstable: id, src_chapter_id, dst_chapter_id, score, status, created_at, reviewed_at, reviewer_note. UNIQUE(src, dst).Tests
83 pass. Existing tests cover DB schema addition and plugin schema validation.
Building on v3.9.0 (PR #4 corpus policy).