Closes#144 (audit line per tools/call) and #145 (origin/written_by stamped on every model-reachable write, surfaced in brain_query/brain_get).
Passed pr-gatekeeper after four rounds of adversarial review, verified by execution: #147
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Co-authored-by: Mathias <mthbqv@gmail.com>
Four call sites did `s[:limit] + "…"`. Both halves are byte operations:
len() counts bytes and a slice cuts bytes. On Swedish text — å ä ö are
two bytes, an em dash three — the cut lands mid-character and leaves an
orphaned lead byte, producing invalid UTF-8.
It failed loudly in exactly one place: wiki/index.md is written raw to
disk, where every strict decoder then rejects the whole file. The other
three (brain_context excerpts x2, brain_query excerpts) feed MCP
responses, where encoding/json silently substitutes U+FFFD — so those had
been quietly eating the last character of truncated excerpts, with no
error anywhere and nothing to notice.
textutil.Truncate counts runes and appends the marker only when content
was actually dropped; an ellipsis on complete text is a lie about the
content. The test is a property, not an example: it walks the limit
across every offset of a multi-byte string and asserts utf8.Valid at each
one, so a future off-by-one cannot pass by picking a lucky boundary.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01BeAp5LTscnz2W7Ubt4eJ6m
Initial M4 mapping put wiki/entities/* in tier=note. Post-M4 eval
regressed qwen35-9b-fast from rank 2 → off top-5: knowledge entries
that cite the entity in passing now outscore the entity page itself
(1.5× weight vs 1.0×).
Entity anchor pages are durable facts about concrete things — they
map cleanly to the knowledge/facts/ slot in the post-M3 layout
target. Promote them now so the path inference matches.
Eval re-run after deploy is in infra#72.
The eval set under brain/eval/qa-2026-05.md showed BM25 top-1 at 20%
with 5 of the missing slugs being short focused knowledge entries
that lost to long aggregate docs on raw term-frequency. Tier weighting
addresses that without touching the BM25 algorithm itself.
How
- Result struct gains a Tier field, populated during the file walk
via extractTier (frontmatter wins, path prefix as fallback —
mirrors the graph.inferTierFromPath logic so the two callers stay
in lockstep).
- After the existing sort (and optional hybridMerge), do a final
stable re-sort by float64(Score) * tierWeight(Tier). Knowledge
×1.5, note ×1.0, inbox ×0.3, unknown ×1.0.
- hydrate() (vector-only hits) also fills Tier so re-ranking covers
the hybrid path.
Test covers the load-bearing case: a long note-tier doc with raw=10
loses to a short knowledge-tier doc with raw=8 after weighting
(8×1.5=12 vs 10×1.0=10).
Measurement gate is in infra#72: re-run brain/eval/score.py against
the live brain after this image lands; close the issue when top-1
hit rate lifts by ≥10 absolute points.
Long markdown files (>~8KB) silently failed to embed because nomic-embed-text
on iguana has a 2048-token context. embed sync logged errors=1 every cycle
with no useful body until #37 added per-item logging — three files exceed
the ceiling: finbert source (8 KB), koala-machine-state (7.1 KB),
litellm-absorption (8.8 KB). Curated knowledge entries should never be
vector-blind.
Approach: chunk-before-embed, no schema change.
vectorstore/chunk.go (new)
- ChunkMarkdown splits at H1/H2 boundaries; sections over maxBytes are
further split at paragraph boundaries, packing greedily under budget.
- NumberChunks assigns "<parent>#NNNN" storage paths (1-based, zero-padded
to 4 digits — handles files with up to ~10k sections in stable sort order).
- ParentPath strips the chunk suffix for retrieval-side dedup.
vectorstore/sync.go
- After ChunkMarkdown produces N pieces, each is embedded + upserted as a
separate brain_embeddings row at "<parent>#NNNN". maxChunkBytes = 4000
(≈1000 nomic tokens, well under the 2048 ceiling with headroom for
unicode/code blocks).
- "Already embedded?" check now reduces known paths to parent set via
ParentPath, so the first chunk hit short-circuits the file.
- Delete walk also reduces via ParentPath; when a parent file disappears,
every chunk row (and any pre-existing bare-path row, for backward
compatibility with rows written before this change) gets dropped.
search/search.go
- hybridMerge collapses chunk-path vector hits to parent via ParentPath
before scope check, RRF accumulation, and hydration. A file with three
chunk hits returns one result row, not three.
Backward compatibility: pre-existing bare-path rows in brain_embeddings
keep working — ParentPath returns them unchanged, knownParents handles
them as if they were "wiki/foo.md#NNNN" hits, sync skips re-embed, and
search dedup is a no-op for them. No migration required to ship.
Tests:
- chunk_test.go covers short / heading split / oversized section /
content preservation / chunk numbering / parent-path stripping.
- sync_test.go adds long-file chunking, single-chunk-row short file,
skip-if-any-chunk-known, delete-all-chunks-of-disappeared-file.
Existing tests updated for #NNNN paths.
- search_test.go adds chunk-paths-dedupe-to-parent.
Closes gitea/mathias/infra#38.
Wires nomic-embed-text (iguana ollama) + pgvector on the shared
postgres18 into brain_query / brain_answer via Reciprocal Rank Fusion.
Pure BM25 stays the default; setting BRAIN_PG_DSN and BRAIN_EMBED_URL
together opts in. Setting one without the other is misconfiguration →
exit 1.
New packages:
- internal/embed
Client.Embed(ctx, text) → []float32 via POST {URL}/api/embed.
Defaults to nomic-embed-text:latest (768 dim). nil-on-empty-URL so
callers gate on a single nil check.
- internal/vectorstore
PGStore wraps a pgxpool against postgres18. Init creates
brain_embeddings(path PK, vector(768), updated_at) + HNSW cosine
index idempotently. Upsert / Delete / Search / KnownPaths.
Sync(brainDir, store, embedder) diffs brain/wiki/ against the store
and upserts new files / deletes removed ones; StartSync runs it on
a ticker (default 300s). Integration tests gated by BRAIN_PG_TEST_DSN.
- scripts/brain-embeddings-init.sql
One-time DBA setup: brain DB, brain_app role, vector extension,
GRANTs. Idempotent.
Search layer:
- search.QueryOptions gains Vector + Embedder fields.
- QueryContext is the cancellable variant; Query stays for callers.
- When both are set, BM25 (top-N) and pgvector (top-4N) candidates
merge via Reciprocal Rank Fusion (k=60, Cormack et al. 2009 — no
tuning knob, robust to scale differences between rankers).
- Vector-only hits are hydrated from disk so callers see uniform
Result records (path, title, excerpt, wing, hall, score).
- Wing/hall filters still apply to vector candidates via path-prefix.
- On embedder/vector errors the search falls back to BM25 — embedding
outage degrades quality but doesn't take the brain offline.
MCP wiring:
- mcp.Server.WithHybridRetrieval(v, e) opt-in setter, same shape as
WithReranker.
- brainQuery and brainAnswer pass the wired vector/embedder through
to search.QueryContext.
REST:
- POST /backfill-embeddings drives Sync synchronously. Returns
{added, deleted, errors[]}. 503 when feature is unconfigured.
cmd/server/main.go:
- BRAIN_PG_DSN + BRAIN_EMBED_URL together enable hybrid; one alone
→ exit 1.
- vectorAdapter bridges *PGStore (returns []Hit) to
search.VectorSearcher (which takes []VectorHit) without either
package importing the other.
- BRAIN_EMBED_SYNC_INTERVAL (default 300s) controls the background
Sync ticker.
Backend pivot from Qdrant to pgvector recorded in DECISIONS.md
2026-05-18 (supersedes 2026-04-08): postgres18 already runs in
databases/ ns, Qdrant was never deployed, one engine beats two.
Dependency: github.com/jackc/pgx/v5 — modern, native pgvector via
parametric vector literals.
Tests:
- embed.Client: empty-URL nil, request shape, dimension, upstream
error propagation, empty-text rejection.
- vectorstore.PGStore: dimension validation (unit); upsert/search/
KnownPaths (integration, BRAIN_PG_TEST_DSN-gated).
- vectorstore.Sync: adds new files, skips known, deletes
disappeared, skips _index.md, no-op when nil, collects embedder
errors.
- search.Query: hybrid promotes vector-only hits via RRF; falls
back to BM25 on embedder error.
Closes hyperguild#8.
Adds a two-dimensional address (wing, hall) to brain notes. A wing is a
topic domain (e.g. jepa-fx, hyperguild); a hall is one of a closed
vocabulary of memory types (facts, decisions, failures, hypotheses,
sources). Notes route to brain/wiki/<wing>/<hall>/<slug>.md with
wing/hall/created_at YAML frontmatter, making the directory a valid
Obsidian vault.
Changes:
- new package ingestion/internal/brain (NotePath, ValidHalls, Sanitise,
BuildWingIndex, BuildAllWingIndexes)
- api.WriteNote refactored to WriteNoteOptions; wing+hall routes to
brain/wiki/, otherwise falls back to brain/knowledge/ (legacy)
- search.Query → QueryOptions with optional Wing/Hall filtering; Result
carries wing/hall extracted from frontmatter or path segments
- MCP tools brain_write and brain_query gain optional wing/hall params
(hall enum-validated); new brain_index tool regenerates _index.md MOC
- POST /index REST endpoint mirrors brain_index
- brain_write auto-rebuilds the wing's _index.md after a wing+hall write
- scripts/migrate-brain-halls.sh migrates flat brain/wiki/{concepts,entities}/
into the new layout (dry-run by default, --commit applies)
All existing tests pass; new tests cover wing/hall write routing, scope
filtering, invalid hall rejection, _index.md generation, and migration
script paths.
Closes hyperguild#1.
Both walk-level errors and ReadFile failures now use best-effort
semantics (warn via slog, continue) instead of mixed abort/silent-skip.
filepath.Rel error is now propagated from the callback instead of
discarded.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Implements search.Query which walks brainDir/wiki/**/*.md, scores files
by term-frequency across query tokens, and returns results sorted by
score descending. Uses only stdlib — no external search deps.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
mathiasandClaude Opus 5.5