research: benchmark hybrid (lexical+dense+graph) recall vs current FTS

Viktor asked to enhance the memory system with 'semantics' — remember concepts
(not just tokens) linked in a graph — and to prove, by benchmarking against the
current system, that it actually improves recall. A multi-phase research workflow
(18 agents) did landscape research, an adversarially-reviewed integration design,
a stratified eval set over the real 5,452-memory corpus, and a head-to-head
prototype-vs-current benchmark.

Result: hybrid (lexical FTS + dense embeddings, RRF-fused) beats FTS on every
overall metric, driven by a robust paraphrase win (recall@10 +0.350). Recommend
adopting lexical+dense; the concept graph is DEFERRED.

Post-run adversarial review correction (applied to all docs before commit): the
prototype's fusion config structurally barred the graph leg from the ranked top-k,
so the 'graph contributes nothing' ablation was a math artifact, NOT an empirical
result — the graph is UNEVALUATED, not disproven (deferred on cost+uncertainty).
Multi-hop deltas are not statistically significant. Glossary in CONTEXT.md; framing
in ADR-0001-0003; findings in ADR-0004-0006 + docs/research/.

Privacy: the corpus/queries/qrels/results are the user's real memories and stay
gitignored (data/, cache/, results/, build_eval_set.py); only harness code,
aggregate numbers, and synthetic examples are committed.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

docs/adr/0006-pgvector-hnsw-halfvec-1024d-embeddings.md

@@ -0,0 +1,49 @@
+# Production vector storage: pgvector HNSW + halfvec(1024); 1024-d embeddings (Voyage-3.5 / bge-large)
+
+Phase 1 of the hybrid ([ADR-0004](0004-phase-the-hybrid-lexical-dense-first-graph-gated.md)) needs a
+production home for the dense embeddings. Per [ADR-0002](0002-api-postgres-first-sqlite-stays-lexical.md)
+that is **pgvector on the shared CNPG Postgres**, where claude-memory is already a database tenant — no new
+datastore.
+
+> ⚠️ **Correction (verified against live infra by a design challenger):** an earlier draft justified this
+> with "Immich already runs pgvector on the same cluster." That is **false** — Immich runs its **own**
+> Postgres, not the shared CNPG — so it is NOT evidence the shared cluster has the extension. **pgvector
+> must be explicitly enabled on CNPG** (extension install, and possibly a CNPG operand-image change) via
+> Terraform **before this can land**; do not assume it is already available.
+
+Decisions:
+
+- **Index: HNSW** (`USING hnsw (embedding halfvec_cosine_ops) WITH (m=16, ef_construction=64)`,
+  query knob `hnsw.ef_search` set via `SET LOCAL` inside the recall txn under PgBouncer). Best
+  speed-recall tradeoff, buildable on an empty table. **IVFFlat rejected** — it must be built *after*
+  data exists (empty-table footgun) and has a lower recall ceiling.
+- **Type: `halfvec(1024)`** (fp16) — halves index size at ~no recall loss; 1024-d halfvec = 2048
+  bytes/row → single-digit MB for the whole corpus.
+- **Dimension fixed at 1024**, chosen **once** (changing it later forces a full re-embed + HNSW
+  rebuild). 1024 matches both the production model (Voyage-3.5) and the prototype model
+  (bge-large-en-v1.5), so the column and all fusion code are identical regardless of model.
+- **Model: Voyage-3.5** (1024-d, hosted) for **non-sensitive** memories (highest measured retrieval
+  quality of the hosted options, free tier covers the corpus); **bge-large-en-v1.5** (1024-d, local,
+  MIT) for **sensitive memories and the no-API-key fallback** ([ADR-0003](0003-external-embedding-apis-allowed-for-non-sensitive-memories.md)).
+  `is_sensitive=1` rows are never embedded externally — `embedding=NULL`, lexical-only.
+- **pgvectorscale / StreamingDiskANN deferred** — Rust/pgrx must be compiled into the CNPG operand
+  image, and it only earns its keep above ~1–5M vectors; our corpus is orders of magnitude below that.
+
+## Migration shape
+
+A single **additive** Alembic migration: `ALTER TABLE memories ADD COLUMN embedding halfvec(1024)`
+(NULL for sensitive) + `CREATE INDEX CONCURRENTLY … USING hnsw …`. The existing generated
+`search_vector tsvector` + GIN index (`migrations/001`) are **untouched**, so lexical behaviour and
+the SQLite-only degrade path are unchanged. pgvector enablement on CNPG and any extension/operand
+change land as **Terraform/Terragrunt** in `infra/stacks/…` (GitOps, never kubectl) and trigger a
+rolling restart of the shared cluster — coordinate accordingly.
+
+## Consequences
+
+- The prototype's in-process numpy matrix maps directly to this column; only the substrate changes,
+  not the retrieval math.
+- The prototype measured **bge-large** quality; a cheap follow-up should re-run the dense leg with
+  **Voyage-3.5** on the non-sensitive corpus to confirm the hosted ceiling holds on our content
+  before locking the production default.
+- Production latency/ANN-approximation/filtered-top-k behaviour are unmeasured in the prototype and
+  must be validated post-migration (a stated benchmark limitation).