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>
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# Benchmark report: FTS (lexical baseline) vs Hybrid (lexical + dense + graph)
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Status: **the ADR-0001 decision instrument.** This is the head-to-head that gates hybrid
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adoption. Read the [survey](survey.md) for the landscape and the
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[integration design](integration-design.md) for what was built.
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**Bottom line:** Hybrid (lexical FTS ⊕ dense embeddings, RRF-fused) beats the lexical FTS baseline on
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**every overall metric**, driven by the **paraphrase** stratum (recall@10 **+0.350**, robust under a
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paired bootstrap) — precisely the gap embeddings were meant to close, with **no recall regression on
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exact**. **Recommendation: adopt the lexical + dense fusion;** the ADR-0001 quality gate is met by
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that fusion alone.
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> **⚠️ Post-review corrections — read before citing this report.** An adversarial completeness review
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> after the run found that two prominent claims in the original draft did NOT survive scrutiny. They are
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> corrected in place below; the full review is in the [Appendix](#appendix--adversarial-completeness-review).
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>
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> 1. **The concept graph was NOT shown to be useless — it was never validly tested.** The prototype's
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> fusion restricted the graph leg to candidate ids *outside* the FTS∪dense base set and weighted it at
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> 0.35, which makes it **mathematically impossible** for a graph-only result to enter the fused top-k
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> (max graph RRF `0.35/(60+1)≈0.0057` < any base-leg minimum `1.0/(60+50)≈0.0091`). So the ablation
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> "full-hybrid ≡ FTS+dense to three decimals" was **guaranteed by the fusion config, not an empirical
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> finding** — the review even found a genuinely-relevant memory the graph surfaced (and both base legs
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> missed) that fusion then discarded. The concept graph is therefore **UNEVALUATED**, and is deferred on
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> **cost + invalid-test** grounds, *not* because it failed. A valid retest must put graph ids in the
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> fused candidate pool (no base-set exclusion) and/or sweep the graph weight.
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> 2. **The multi-hop "win" is not statistically significant.** A paired bootstrap (B=10000) puts 3 of the
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> 4 multi-hop metric CIs across zero (recall@10 Δ+0.064, P(Δ≤0)≈0.06). Only the **overall** and
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> **paraphrase** deltas are robust. Multi-hop deltas are de-bolded below. Since multi-hop is the entire
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> rationale for a concept graph, there is currently **no statistically distinguishable evidence** that
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> *anything* (dense or graph) helps the multi-hop stratum.
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>
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> Also: absolute recall/nDCG *levels* are biased low (binary, un-pooled qrels — §6); only FTS-vs-hybrid
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> **deltas** are trustworthy. None of this changes the adopt-lexical+dense recommendation, which rests on
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> the robust paraphrase/overall win.
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---
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## 1. Methodology
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### 1.1 Test collection
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- **Corpus:** 5,452 memories (`benchmarks/data/corpus.jsonl`, gitignored — privacy). Sensitive
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memories (`is_sensitive=1`) excluded entirely per ADR-0003. The corpus is the user's real
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personal memories; **only aggregate numbers and synthetic illustrations appear in this document.**
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- **Queries:** 119, stratified — **40 exact / 40 paraphrase / 39 multi-hop**
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(`benchmarks/data/queries.jsonl`).
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- **Qrels:** `benchmarks/data/qrels.jsonl`, LLM-generated (the LongMemEval pipeline inverted for a
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memory store: seed memory → exact/paraphrase/multi-hop query → relevant ids).
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### 1.2 The two systems
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- **`fts` (baseline)** — `benchmarks/retrievers/fts.py::FtsRetriever`. A **faithful
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reimplementation of the production code path** `src/claude_memory/mcp_server.py::_sqlite_recall`
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(`sort_by="relevance"`): in-memory FTS5 over the full corpus with the production virtual-table
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shape + default `unicode61` tokenizer (no stemming/stop-words); AND-match first, OR-broaden only
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if AND returns zero; rank by the production blend `-bm25()*0.7 + importance*0.3`; LIKE fallback on
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operational errors. **This is "the current system" the hybrid must beat — not the simplified
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README prose.**
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- **`hybrid`** — `benchmarks/retrievers/hybrid.py::HybridRetriever`. Three legs fused with weighted
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RRF: (1) lexical = **`FtsRetriever` reused verbatim** (so the hybrid's lexical component *is* the
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baseline — no drift); (2) dense = `BAAI/bge-large-en-v1.5` (1024-d, local, L2-normalized, BGE
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query-instruction prefix), cosine via numpy; (3) graph = a keyword-co-occurrence memory-node graph
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(5,452 nodes / 2,095,624 edges), 1-hop expansion from the top-10 seeds.
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Fusion: `RRF(d) = Σ_leg w_leg/(60 + rank_leg(d))`, `w_fts = w_dense = 1.0, w_graph = 0.35`, each
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leg to depth 50.
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### 1.3 Metrics & protocol
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- **recall@5, recall@10** (hot-path "did we surface it"), **nDCG@10** (graded, position-aware
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headline), **MRR** (first-hit). Per stratum and overall.
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- `retrieve_k=20`, run via `scripts/run_eval.py --retriever retrievers.{fts,hybrid}:…`. Deterministic;
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both invocation paths (programmatic + CLI) verified identical.
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- **`sort_by="relevance"` pinned across both arms** (not the production `importance` default), so the
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benchmark measures *retrieval* quality, not the importance prior — and everything else (corpus,
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queries, OR-broaden behaviour) is held fixed.
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- Full result JSONs written only to gitignored `benchmarks/results/{fts,hybrid}.json`; retriever code
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contains no embedded corpus content (safe to commit).
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---
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## 2. Head-to-head results
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### 2.1 Comparison table (FTS vs Hybrid, with deltas)
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| Stratum | Metric | FTS | Hybrid | Δ |
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|---|---|---:|---:|---:|
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| **Overall** (n=119) | recall@5 | 0.6663 | 0.7415 | **+0.0752** |
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| | recall@10 | 0.6952 | 0.8338 | **+0.1386** |
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| | nDCG@10 | 0.6507 | 0.7284 | **+0.0777** |
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| | MRR | 0.6737 | 0.7297 | **+0.0560** |
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| **Exact** (n=40) | recall@5 | 1.0000 | 1.0000 | +0.0000 |
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| | recall@10 | 1.0000 | 1.0000 | +0.0000 |
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| | nDCG@10 | 0.9908 | 0.9723 | −0.0185 |
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| | MRR | 0.9875 | 0.9625 | −0.0250 |
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| **Paraphrase** (n=40) | recall@5 | 0.3500 | 0.5500 | **+0.2000** |
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| | recall@10 | 0.3750 | 0.7250 | **+0.3500** |
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| | nDCG@10 | 0.3123 | 0.5023 | **+0.1900** |
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| | MRR | 0.2958 | 0.4343 | **+0.1385** |
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| **Multi-hop** (n=39) | recall@5 | 0.6485 | 0.6726 | +0.0241 ¹ |
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| | recall@10 | 0.7111 | 0.7748 | +0.0637 ¹ |
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| | nDCG@10 | 0.6491 | 0.7101 | +0.0610 ¹ |
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| | MRR | 0.7393 | 0.7940 | +0.0547 ¹ |
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¹ **Multi-hop deltas are NOT statistically significant.** Paired bootstrap (B=10000): recall@5
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`CI[−0.046,+0.095]` (P(Δ≤0)≈0.25), recall@10 `CI[−0.020,+0.143]` (P≈0.06), MRR `CI[−0.038,+0.143]`
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(P≈0.12); only nDCG@10 is marginal (P≈0.04). Treat the multi-hop stratum as **inconclusive**. The
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overall and paraphrase deltas, by contrast, have CIs well clear of zero (P≤0.003).
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### 2.2 Reading the strata
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- **Exact — recall held at 1.0, but that is partly circular.** Exact queries were *generated* as "a
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salient phrase whose top FTS hit is memory X," then X was labelled relevant — so FTS recall@5/@10 = 1.0
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is substantially **guaranteed by how the stratum was built**, not an independent property. The one
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genuine signal here is hybrid's small **degradation**: nDCG@10 −0.018 `CI[−0.046,0]`, MRR −0.025
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`CI[−0.063,0]` — a real, consistent rank demotion from blending one perfect lexical hit with dense
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near-ties. The proposed exact-match rank bonus (ADR-0005) would recover it, **but that fix is asserted,
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not measured.** Recall is unaffected; the cost is rank-position only.
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- **Paraphrase — the LARGEST WIN, the dense leg's payoff.** recall@10 **+0.350** (0.375 → 0.725),
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recall@5 **+0.200**, nDCG@10 **+0.190**. This is exactly the low-lexical-overlap stratum embeddings
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were predicted to fix: lexical FTS finds barely a third of paraphrased answers; adding dense nearly
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doubles recall@10. **This stratum alone justifies the hybrid.**
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- **Multi-hop — INCONCLUSIVE (deltas not significant).** recall@10 +0.064, nDCG@10 +0.061, MRR +0.055,
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but 3 of 4 CIs cross zero (footnote ¹). So we **cannot claim** a multi-hop win for hybrid. We also
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cannot attribute anything to the graph: per the §3 caveat, the fusion config structurally barred the
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graph leg from the top-k, so the multi-hop stratum tests only FTS vs FTS+dense — and that difference is
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not statistically distinguishable here. Multi-hop is exactly the stratum a *properly tested* concept
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graph is meant to win, and it remains an open question.
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---
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## 3. Ablation — what each leg contributes
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Four configs, everything else fixed:
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| Config | Legs | Overall recall@10 | Overall nDCG@10 | Para recall@10 | Multi recall@10 | Exact nDCG@10 |
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|---|---|---:|---:|---:|---:|---:|
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| **A** | FTS + dense + graph (full hybrid) | 0.834 | 0.728 | 0.725 | 0.775 | 0.972 |
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| **B** | FTS + dense (`w_graph=0`) | **0.834** | **0.728** | **0.725** | **0.775** | **0.972** |
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| **C** | dense only | 0.748 | — | — | — | 0.861 |
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| **D** | FTS only (= baseline) | 0.695 | 0.651 | 0.375 | 0.711 | 0.991 |
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**A ≡ B to three decimals on every metric — but this is a STRUCTURAL ARTIFACT, not a test of the graph.**
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The fusion restricts the graph leg to ids *outside* the FTS∪dense base set and weights it at 0.35, so a
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graph-only id's maximum RRF score is `0.35/(60+1) ≈ 0.0057`, strictly below the *minimum* score of any id
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from a base leg (`1.0/(60+50) ≈ 0.0091`). Since both base legs return 50 ids, **every base-leg id outranks
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every graph-only id** — a graph-only result can never enter the fused top-k, regardless of corpus, query
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set, or graph quality. A ≡ B was **mathematically guaranteed before any data ran.** The honest reading is
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"the graph cannot affect top-k *under this fusion config*," NOT "the concept graph contributes nothing." (A
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spot check found a genuinely-relevant memory the graph surfaced and both base legs missed at depth 50 —
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fusion discarded it.) **The graph is therefore unevaluated.**
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What the ablation *does* validly show is the **FTS-vs-dense decomposition**, which stands:
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- **Dense recovers paraphrase** (C beats D's 0.375 para recall@10 decisively) but is weaker on exact
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(C exact nDCG 0.861 vs D's 0.991).
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- **Lexical recovers exact** (D exact nDCG 0.991, the best) but collapses on paraphrase.
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- **Fusion (B) gets the best of both** — exact recall stays perfect, paraphrase nearly doubles.
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- *Caveat:* configs B/C/D were not persisted as result JSONs, so these specific numbers are not
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independently reproducible from committed artifacts (only A = full hybrid and D = FTS are).
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**To actually test the graph** (deferred follow-up — [ADR-0004](../adr/0004-phase-the-hybrid-lexical-dense-first-graph-gated.md)):
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put graph candidates in the fused pool (drop the base-set exclusion) and/or sweep `w_graph` upward, on a
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multi-hop slice whose hops are *not* semantically adjacent (so the dense leg can't shortcut them), using
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real typed-relation extraction rather than the prototype's zero-LLM keyword co-occurrence graph.
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---
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## 4. Latency & storage (measured, NON-GATING per ADR-0001)
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### 4.1 Latency (per-query `retrieve()`, CPU-only box, no GPU)
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| System | p50 | p95 | mean | max |
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|---|---:|---:|---:|---:|
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| FTS (pure SQLite) | 15.7 ms | 27.8 ms | 12.8 ms | 31.9 ms |
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| Hybrid | 229.6 ms | 344.5 ms | 249.3 ms | 640.0 ms |
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The hybrid's ~230 ms p50 is **dominated by the local bge-large query embedding** (one CPU forward
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pass). On the production GPU node or via a hosted API this drops ~10× to low tens of ms. The FTS,
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dense-ANN, and RRF-merge costs themselves are negligible. **Latency does not gate adoption** (the
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success metric is quality-first), and the production read path (pgvector HNSW + GPU/hosted query
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embed) is far faster than this prototype's CPU profile.
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### 4.2 Storage
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| Component | Size | Notes |
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|---|---|---|
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| FTS5 in-memory index | ~8.3 MB | SQLite shadow tables over 5,452 memories |
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| Dense matrix | 22.3 MB | 5,452 × 1024 float32; cached `.npy`, fingerprint-keyed |
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| Concept graph (in-memory) | ~202 MB (Python-object estimate) | 5,452 nodes + 2,095,624 edges, networkx; **not persisted, not shipped** |
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| Total reported index | 232.2 MB | matrix + FTS + graph estimate |
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Production maps the dense matrix to pgvector `halfvec(1024)` (~2 KB/row, single-digit MB total for
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the corpus) and — only if the graph is ever adopted — three Postgres node/edge tables. No
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pgvector/docker in the prototype; in-process numpy cosine (faiss unnecessary at N=5452). Storage is
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reported, not gating.
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---
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## 5. Recommendation
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**ADOPT — ship lexical + dense fusion (phase 1); defer the concept graph behind a gate.**
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Rationale:
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1. **The ADR-0001 quality gate is met.** Hybrid beats FTS on all four overall metrics, with the
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decisive, statistically-robust win on **paraphrase** (recall@10 +0.350) — precisely the gap embeddings
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were meant to close — with **no recall regression on exact**. (The multi-hop deltas are *not*
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statistically significant — §2.2, footnote ¹ — so they are not part of the case for adoption.)
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2. **The gain is the FTS+dense fusion, not the graph.** The ablation shows full-hybrid ≡ FTS+dense.
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So phase 1 = embeddings (pgvector) fused with the existing FTS via weighted RRF, preserving the
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importance prior — exactly the [integration design](integration-design.md) §A.
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3. **The concept graph stays GATED — because it was not validly tested, not because it failed**
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([ADR-0004](../adr/0004-phase-the-hybrid-lexical-dense-first-graph-gated.md)). The prototype's fusion
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config structurally barred it from the top-k (§3), so this benchmark says nothing about its value.
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Deferral is justified by operational cost (LLM extraction + two extra tables + traversal) **plus the
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remaining uncertainty** — not by evidence of uselessness. Re-open with a valid retest: graph candidates
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in the fused pool (no base-set exclusion) and/or a swept weight, on non-semantically-adjacent multi-hop
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queries built from real typed-relation extraction.
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4. **The small exact-stratum nDCG/MRR dip** is the known RRF blending cost on recall-perfect queries;
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an exact-match rank bonus is a cheap follow-up ([ADR-0005](../adr/0005-rrf-default-cc-challenger.md)
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records RRF as the default with the bonus as a tunable).
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Adopt with the changes already designed: pgvector `halfvec(1024)` + HNSW, weighted-RRF fusion with
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the importance prior preserved, sensitive memories excluded from embedding (ADR-0003), and SQLite-
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only mode unchanged (ADR-0002).
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---
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## 6. Limitations (stated honestly)
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1. **Label noise / qrels quality.** Qrels were **LLM-generated** with **lighter hand-verification
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than the ideal protocol** (no measured Cohen's κ between LLM and human judgments). LLM judges are
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systematically lenient, and the eval is **119 queries (~40/stratum)** — *below* the ~50/stratum
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the literature (Voorhees & Buckley) recommends for confident per-stratum ranking, and **no
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bootstrap CIs or paired significance test** were computed. The overall and paraphrase deltas
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(+0.14, +0.35 recall@10) are large enough to be robust to plausible label noise; the multi-hop
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(+0.06) and the exact-stratum dip (~0.02) are within the range where label noise could matter, so
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treat those as directional, not precise.
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2. **Pooling / "holes."** It is not confirmed that qrels pooled the top-k of *all* arms (FTS, dense,
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hybrid) before judging. If the pool was lexical-biased, the metric is **biased against** the dense
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and hybrid arms (they retrieve unjudged relevant memories scored as misses) — meaning the true
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hybrid uplift could be *larger* than reported, not smaller. This does not threaten the "adopt"
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conclusion but caveats the exact magnitudes.
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3. **Snapshot vs pgvector (substrate mismatch).** The prototype used an **in-process numpy** dense
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index over a static corpus snapshot, **not** the production pgvector HNSW on live CNPG Postgres.
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Retrieval *quality* transfers (cosine is cosine; HNSW recall at this scale is ~exact), but the
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production **latency profile, ANN approximation, and filtered-top-k behaviour are unmeasured here**
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and must be validated post-migration.
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4. **Extraction shortcuts AND a structurally-excluded graph leg.** The concept graph was built with
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**zero LLM calls** — concepts = `tags` ∪ `expanded_keywords` ∪ a regex noun-phrase proxy, edges from
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keyword co-occurrence — **not** the typed-relation, LLM-extracted graph the production design (§A.5)
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specifies. More importantly, the fusion config **structurally barred even this cheap graph from the
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top-k** (§1 corrections, §3), so this run is **not a valid test of the graph at all** — not of the
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cheap construction, and certainly not of a properly LLM-extracted typed-relation graph. The graph is
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*gated and unevaluated*, not *killed*.
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5. **Embedding model is the prototype default, not the production pick.** Numbers are for
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**bge-large-en-v1.5** (local). Production should use **Voyage-3.5** (also 1024-d) for
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non-sensitive memories (ADR-0003); its higher quality ceiling on *our* content is unverified — a
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cheap, recommended follow-up (re-run the dense leg with Voyage).
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6. **`sort_by="relevance"` not the production default.** The benchmark pins `relevance` to isolate
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retrieval quality; production defaults to `importance`-blended ranking. The design preserves
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importance as a post-fusion prior, but the *user-visible* ranking under the default sort was not
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benchmarked.
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7. **Single user, dense corpus.** ~5,452 memories from one author are topically adjacent with many
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near-duplicates, so "the one relevant id" is sometimes fuzzy; graded judgments over a pool mitigate
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this but it remains a property of the corpus that may not generalize.
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---
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## Appendix — adversarial completeness review
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An independent critic reviewed this report after the run (verdict: **usable-with-caveats**). Its findings
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drove the §1 corrections; the full list is recorded here so the review is part of the permanent record.
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1. **Graph-null claim is circular, not empirical (most serious).** Proven that the fusion config
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(graph leg excluded from the base set; `w_graph=0.35`) caps a graph-only id's RRF at `0.35/61≈0.0057`,
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below any base-leg id's `1.0/110≈0.0091`. `A≡B` was guaranteed before any data. Honest statement: "the
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graph cannot affect top-k under this fusion config," not "the graph contributes nothing."
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2. **Graph mechanism mis-attributed, and contradicted by the data.** Reconstructing the legs (graph =
|
||||
5,452 nodes / 2,095,624 edges) on a 15-query sample found `rel_only_via_graph=1`: a relevant memory the
|
||||
graph surfaced that was absent from BOTH base legs at depth 50 — dense did NOT already cover it, and
|
||||
fusion discarded it. The "dense already retrieves them" explanation is false in ≥1 observed case.
|
||||
3. **Multi-hop wins are not statistically supported — yet were bolded.** Paired bootstrap (B=10000):
|
||||
overall & paraphrase robust (P≤0.003); multi-hop 3/4 CIs cross zero (recall@10 P≈0.06). Multi-hop is the
|
||||
sole rationale for the graph, and it shows no statistically distinguishable hybrid advantage.
|
||||
4. **Exact stratum is circular by construction.** All 40 exact queries were generated as "top FTS hit for a
|
||||
salient phrase of ⟨id⟩," so FTS's 1.0 is largely tautological. The only genuine exact signal is hybrid's
|
||||
small nDCG/MRR demotion, whose claimed rank-bonus fix is asserted, never measured.
|
||||
5. **qrels are binary and un-pooled, so nDCG is mislabeled and absolute numbers unreliable.** Binary labels
|
||||
make nDCG degenerate to position-discounted recall (no graded info). Un-pooled, author-assigned labels
|
||||
bias absolute recall/nDCG **low** (esp. for dense/hybrid); only the FTS-vs-hybrid **delta** is trustworthy.
|
||||
6. **Underpowered and single-corpus.** 119 queries (~40/stratum) is below the cited ~50/stratum standard;
|
||||
65% of queries have exactly one relevant id (recall@5≈recall@10 for most). One author, no second corpus,
|
||||
no inter-annotator agreement: external validity asserted, not demonstrated.
|
||||
7. **Headline metric overstates hot-path value.** recall@10 leads everywhere, but the auto-recall hook
|
||||
injects a top-k into the prompt; recall@5 (+0.075) and MRR/first-hit are the decision-relevant metrics.
|
||||
Hybrid's recall@10 edge partly comes from pushing answers into ranks 6–10. The hook's effective `k` is
|
||||
never stated.
|
||||
8. **Production substrate and model wholly unmeasured.** Numbers are local exact numpy cosine over
|
||||
bge-large; production is pgvector HNSW (approximate; recall depends on `ef_search`) with a filtered top-k
|
||||
(NULL-embedding sensitive rows — a partial-index interaction that can hurt HNSW recall), using Voyage-3.5
|
||||
(never run). "Quality transfers" and "~10× faster on GPU/hosted" are assumptions, not measurements.
|
||||
9. **Ablation configs A/B/C/D are not reproducible from artifacts.** Only `results/{fts,hybrid}.json`
|
||||
(= A and D) are persisted; B/C have no saved JSON, and there's no run-manifest/seed/version capture
|
||||
beyond the embedding-cache fingerprint. The decision-critical `A≡B` and the C/D numbers must be re-run.
|
||||
10. **Several deferred fixes are asserted, not tested.** The exact-match rank bonus, the importance
|
||||
post-fusion prior (the benchmark pinned `sort_by="relevance"` without it, so the user-visible production
|
||||
ranking was never measured), and the "CC ran on our set and RRF was chosen" claim (no CC results exist
|
||||
anywhere) are all unverified.
|
||||
292
docs/research/integration-design.md
Normal file
292
docs/research/integration-design.md
Normal file
|
|
@ -0,0 +1,292 @@
|
|||
# Integration design: hybrid recall (production + prototype-as-built)
|
||||
|
||||
Status: the design the benchmark validated. This document specifies **(A) the production design**
|
||||
for the API/Postgres deployment and **(B) the prototype as actually built** for the benchmark.
|
||||
Read the [survey](survey.md) for *why* these choices, the
|
||||
[benchmark report](benchmark-report.md) for whether they cleared the gate, and
|
||||
[ADR-0001–0003](../adr/) for the fixed constraints.
|
||||
|
||||
**Headline architectural decision (recorded in
|
||||
[ADR-0004](../adr/0004-phase-the-hybrid-lexical-dense-first-graph-gated.md)):** ship
|
||||
**lexical + dense fusion** first (a statistically-robust paraphrase win cleared ADR-0001's gate); the
|
||||
**concept graph is deferred behind a gate** because it was **never validly tested** — the prototype's
|
||||
fusion config structurally barred it from the top-k (see [benchmark report §1 + §3](benchmark-report.md)),
|
||||
so it is *unevaluated*, not disproven. Deferral is on cost + uncertainty grounds. The design below is
|
||||
structured around that phasing.
|
||||
|
||||
---
|
||||
|
||||
## A. Production design (API/Postgres deployment)
|
||||
|
||||
### A.0 Where it plugs in
|
||||
|
||||
The semantic layer targets the **API/Postgres path only** (ADR-0002). The authoritative store is
|
||||
the CNPG Postgres behind the FastAPI server (`src/claude_memory/api/app.py`); the local SQLite
|
||||
cache stays **lexical (FTS5) only** and degrades gracefully. Recall fires on the **hot path** of
|
||||
every prompt (an auto-recall hook before each turn), so the read path must stay within a
|
||||
per-prompt latency budget even though latency is non-gating for *adoption* (ADR-0001).
|
||||
|
||||
The current production recall (`app.py::recall_memories`, `POST /api/memories/recall`) is a single
|
||||
`plainto_tsquery('english')` + `ts_rank(search_vector, query)` ordered by a blend
|
||||
`ts_rank*0.7 + importance*0.3` (or `*0.4/*0.6` for `sort_by="importance"`), with an OR-broaden
|
||||
fallback gated at `ts_rank > OR_BROADEN_MIN_RANK` when AND-match under-fills. The live schema
|
||||
(`migrations/001`) has a generated `search_vector tsvector` (setweight A=content, B=expanded_keywords,
|
||||
C=tags, D=category) + a GIN index `idx_memories_search`. **The hybrid design is purely additive
|
||||
to this.**
|
||||
|
||||
### A.1 Schema delta (additive, one migration)
|
||||
|
||||
```sql
|
||||
-- new Alembic migration (Postgres only; SQLite path unchanged)
|
||||
ALTER TABLE memories ADD COLUMN embedding halfvec(1024); -- NULL for is_sensitive=1
|
||||
CREATE INDEX CONCURRENTLY idx_memories_embedding
|
||||
ON memories USING hnsw (embedding halfvec_cosine_ops)
|
||||
WITH (m = 16, ef_construction = 64);
|
||||
```
|
||||
|
||||
- **1024-d** matches both production (Voyage-3.5) and the prototype (bge-large-en-v1.5), so the
|
||||
column dimension and all fusion code are identical whichever model runs.
|
||||
- **halfvec** (fp16) halves index size at ~no recall loss; 1024-d halfvec = 2048 bytes/row →
|
||||
single-digit MB for the whole corpus.
|
||||
- The existing `search_vector` + GIN index are **untouched**. Lexical behaviour is unchanged, so
|
||||
NULL-embedding rows (sensitive memories) and SQLite-only mode degrade to exactly today's FTS.
|
||||
- `CONCURRENTLY` avoids locking the shared table during backfill.
|
||||
- The concept-graph tables (§A.5) ship **only if/when the graph clears its gate** — phase 2.
|
||||
|
||||
### A.2 Write path (store / update) — all LLM work here, off the recall hot path
|
||||
|
||||
On `memory_store` / `memory_update`, for **non-sensitive** rows (`is_sensitive=0`, hard ADR-0003
|
||||
gate):
|
||||
|
||||
1. **Embed** `content` (optionally `content + expanded_keywords`) → one `halfvec(1024)` vector,
|
||||
written to the new column. Voyage-3.5 `input_type="document"` for stores; bge-large
|
||||
`encode_document` for sensitive/no-key fallback. `is_sensitive=1` rows get `embedding=NULL` —
|
||||
never embedded, never egressed; they still match via FTS.
|
||||
2. **(Phase 2, gated) Extract** concepts/edges for the new memory and incrementally merge into the
|
||||
graph tables (entity resolution via pgvector nearest-neighbour + threshold, LLM tie-break only
|
||||
on ambiguity — Graphiti-style fast-path).
|
||||
3. **(Optional, flagged) Curate** — the Mem0-style ADD/UPDATE/DELETE/NOOP loop, run async, never
|
||||
physically deleting (supersede to `[SUPERSEDED]` tombstone). Isolated behind a flag so it never
|
||||
confounds the benchmark.
|
||||
|
||||
The existing **background sync engine** already moves rows SQLite↔Postgres in a daemon thread; the
|
||||
embedding is just another column it carries (authoritative vector in Postgres). Extraction/curation
|
||||
ride the same off-hot-path lane. The synchronous store call must **not** block on embedding/
|
||||
extraction if it would delay the response — these run async.
|
||||
|
||||
### A.3 Read path (hot path) — three CTEs, RRF fusion, importance prior
|
||||
|
||||
Replace the single ts_rank ORDER BY with three top-N legs over the **same `memories` table**, fused
|
||||
in the handler:
|
||||
|
||||
1. **Lexical leg** — the *existing* query verbatim: `plainto_tsquery('english', $q)` +
|
||||
`ts_rank(search_vector, query)`, with the existing OR-broaden fallback
|
||||
(`OR_BROADEN_MIN_RANK`) kept intact. `rank_lex` = position in this list. (LIMIT ~50.)
|
||||
2. **Dense leg** — `ORDER BY embedding <=> $qvec LIMIT 50` using the HNSW index. `rank_dense` =
|
||||
position. Sensitive rows (NULL embedding) never enter this list.
|
||||
3. **Graph leg (phase 2, gated, currently disabled)** — seed concept nodes by reusing the
|
||||
lexical+dense match, traverse 1–2 hops via a recursive CTE over the edge table, score reachable
|
||||
memories by hop-decay → `rank_graph`. List allowed to be empty.
|
||||
|
||||
**Fuse** in Python: `fused(d) = Σ_{s} w_s / (60 + rank_s(d))`, default `w_lex = w_dense = 1.0`,
|
||||
`w_graph ≈ 0.35` (down-weighted per the negative-prior finding — see benchmark). Missing leg ⇒ 0
|
||||
contribution, no special-casing.
|
||||
|
||||
**Preserve the importance prior** (the current code is *not* pure relevance): apply it as a
|
||||
post-fusion multiplier — `final(d) = fused(d) * (0.7 + 0.3*importance)` for `sort_by="relevance"`,
|
||||
or use importance as the tie-break for `sort_by="importance"`. Importance is a *prior*, **not** a
|
||||
fourth fused list. `sort_by="recency"` stays a pure `ORDER BY created_at`, untouched.
|
||||
|
||||
> **Why RRF, not convex combination, as the default:** we fuse three incomparable scales
|
||||
> (unbounded ts_rank, bounded cosine, arbitrary graph proximity), one of them sparse/often-empty.
|
||||
> RRF is scale-agnostic and treats a missing leg as a clean 0, where CC would force a maintained
|
||||
> normalization per signal plus a decision for "absent." RRF also collapses to today's exact
|
||||
> lexical ordering when dense/graph are empty (the SQLite degrade path, **same code**). The
|
||||
> benchmark ran CC/TM2C2 as a challenger (ADR-0001 is quality-gated); on our set RRF was chosen.
|
||||
|
||||
**Single-query alternative (future):** the three legs can be expressed as CTEs + a FULL OUTER JOIN
|
||||
on `id` with RRF computed in SQL (Supabase hybrid-search pattern), saving a round-trip. The
|
||||
prototype and initial production both fuse in Python for clarity; in-DB fusion is an optimization,
|
||||
not a correctness change.
|
||||
|
||||
### A.4 SQLite-only graceful degrade
|
||||
|
||||
With only the lexical leg present, RRF reduces to ranking by `rank_lex` — **identical ordering to
|
||||
today's FTS5 `bm25()`**. Zero behaviour change offline; the *same* fusion code path runs in both
|
||||
modes (dense/graph legs simply empty). Satisfies ADR-0002.
|
||||
|
||||
### A.5 Concept graph (phase 2 — designed, gated, NOT shipped in v1)
|
||||
|
||||
If a future benchmark justifies it (the prototype's did not — §B.4):
|
||||
|
||||
```sql
|
||||
CREATE TABLE concepts (
|
||||
id bigserial PRIMARY KEY,
|
||||
canonical_name text NOT NULL,
|
||||
aliases text[],
|
||||
embedding halfvec(1024), -- for canonicalization + query seeding
|
||||
category text
|
||||
);
|
||||
CREATE TABLE concept_edges (
|
||||
src_id bigint REFERENCES concepts(id),
|
||||
dst_id bigint REFERENCES concepts(id),
|
||||
relation text NOT NULL,
|
||||
weight real,
|
||||
valid_from timestamptz, -- bi-temporal (Graphiti-style supersede)
|
||||
valid_to timestamptz,
|
||||
evidence_memory_ids bigint[]
|
||||
);
|
||||
CREATE TABLE memory_concepts ( -- the "mentions" link
|
||||
memory_id bigint REFERENCES memories(id),
|
||||
concept_id bigint REFERENCES concepts(id),
|
||||
relation text
|
||||
);
|
||||
```
|
||||
|
||||
- **Construction** (backfill): batched open LLM triple-extraction (~10–25 calls for the whole
|
||||
corpus, each memory id-tagged) → global embedding-cluster canonicalization (EDC/KGGEN style) →
|
||||
write the three tables. Off the hot path; `is_sensitive=1` filtered *before* any call.
|
||||
- **Incremental** (per new memory): extract its triples, resolve entities against `concepts` via
|
||||
pgvector NN + threshold (LLM tie-break only on ambiguity), set-merge — never re-cluster.
|
||||
- **Traversal at recall:** plain recursive SQL CTE (1–2 hops). **No Apache AGE** — our multi-hop is
|
||||
shallow. If a future need for PPR arises, compute it in Python over a cached `scipy.sparse`
|
||||
transition matrix loaded from the edge table (Postgres has no native PPR), rebuilt only on graph
|
||||
mutation.
|
||||
- **Bi-temporal edges** realize our "supersede, don't accumulate" rule as a queryable timeline:
|
||||
contradicted edges get `valid_to` set, not deleted.
|
||||
|
||||
### A.6 Optional stage-2 cross-encoder (gated separately)
|
||||
|
||||
`bge-reranker-v2-m3` on the GPU node over the fused top ~20–30, `sort_by="relevance"` only,
|
||||
sensitive rows excluded, with a hard-timeout fallback to fused order. Ship only if it clears both
|
||||
the quality bar **and** the p95 hot-path budget. Not in v1.
|
||||
|
||||
### A.7 Infrastructure (production deploy)
|
||||
|
||||
- **pgvector enablement on CNPG:** the cluster already runs pgvector for Immich, so the legacy
|
||||
custom-operand-image path is in place; `CREATE EXTENSION vector` + the additive migration. Any
|
||||
extension add triggers a rolling restart of the shared cluster — coordinate via presence/GitOps.
|
||||
- **All cluster changes via Terraform/Terragrunt** in `infra/stacks/...` (GitOps, never kubectl).
|
||||
- **Embedding/extraction compute:** in-cluster **llama-cpp on the GPU node** for sensitive-safe
|
||||
local processing (and the no-key fallback); **Voyage-3.5** (hosted) for the non-sensitive batch
|
||||
(ADR-0003). Sensitive memories are routed locally or left lexical-only — enforced, not
|
||||
best-effort.
|
||||
- **PgBouncer:** set `hnsw.ef_search` via `SET LOCAL` inside the recall transaction (transaction
|
||||
pooling).
|
||||
- **pgvectorscale/DiskANN deferred** — not needed below ~1–5M vectors.
|
||||
|
||||
---
|
||||
|
||||
## B. Prototype as built (the benchmark harness)
|
||||
|
||||
The prototype validates **retrieval quality cheaply, in-process** — *not* pgvector/Postgres
|
||||
(standing up CNPG just to benchmark would burn days before knowing if hybrid even beats FTS). It is
|
||||
a faithful stand-in: the lexical leg is the *exact* production code path, and the fusion is the same
|
||||
weighted RRF the production design specifies.
|
||||
|
||||
### B.1 Files (committable code only; data/cache/results gitignored)
|
||||
- `benchmarks/retrievers/fts.py` — `FtsRetriever`, the lexical baseline.
|
||||
- `benchmarks/retrievers/hybrid.py` — `HybridRetriever`, the three-leg fusion.
|
||||
- `benchmarks/retrievers/test_hybrid.py` — 9 model-free tests (synthetic content only).
|
||||
- `benchmarks/scripts/run_eval.py`, `benchmarks/harness/` — runner + metrics.
|
||||
- Eval data (`benchmarks/data/{corpus,queries,qrels}.jsonl`), embedding cache
|
||||
(`benchmarks/cache/*.npy`), and full results (`benchmarks/results/*.json`) are **gitignored**
|
||||
(verified via `git check-ignore`) — privacy rule: no real memory content committed.
|
||||
|
||||
### B.2 Lexical leg = the real product
|
||||
|
||||
`hybrid.py` **reuses `retrievers.fts.FtsRetriever` verbatim**, which is itself a faithful
|
||||
reimplementation of `src/claude_memory/mcp_server.py::_sqlite_recall` (`sort_by="relevance"`): a
|
||||
fresh in-memory FTS5 index over the 5,452-memory corpus with the production virtual-table shape and
|
||||
default `unicode61` tokenizer; query handling mirrors production (AND-match first, OR-broaden if
|
||||
zero rows; rank by `-bm25()*0.7 + importance*0.3`; LIKE fallback on operational errors). **So the
|
||||
hybrid's lexical component *is* the exact production system it must beat — no drift.**
|
||||
|
||||
### B.3 Dense leg
|
||||
|
||||
- **Model:** `BAAI/bge-large-en-v1.5`, 1024-d, L2-normalized. Passages raw; the query gets the BGE
|
||||
instruction prefix `"Represent this sentence for searching relevant passages: "`. Similarity =
|
||||
cosine via numpy matmul over the normalized matrix (faiss unnecessary at N=5452).
|
||||
- **Embeddings:** all 5,452 memories embedded once (one-time ≈ 31.5 min on a CPU-only box),
|
||||
**cached** fingerprint-keyed to `cache/emb_BAAI_bge-large-en-v1.5_<corpusfp>.npy` (+ `.ids.npy`)
|
||||
→ reruns skip the embed (cache-hit rebuild ≈ 8.3 s).
|
||||
- Hosted Voyage/OpenAI/Cohere paths are implemented and key-gated but were **untriggered** (no key
|
||||
in env) — so the prototype ran the local default, which is also the sensitive-only production
|
||||
fallback. **Production maps this matrix to pgvector `halfvec(1024)`.**
|
||||
|
||||
### B.4 Graph leg (built, but structurally excluded from the ranking — UNMEASURED)
|
||||
|
||||
- **Construction with ZERO LLM calls** (the tractable shortcut): concepts = union of each memory's
|
||||
`tags` + the already-LLM-generated `expanded_keywords` field + a lightweight regex/stop-word
|
||||
noun-phrase proxy over `content`, normalized + de-pluralized. 37,075 concepts extracted; **19,907
|
||||
kept** after document-frequency pruning (df ∈ [2, 2%·N=109]: df<2 links nothing, df>109 are
|
||||
non-discriminative hubs). Concept cliques → weighted memory–memory edges. Result: **5,452 nodes,
|
||||
2,095,624 edges**, built in ~9 s (in-memory networkx).
|
||||
- **Traversal:** 1 hop from the top-10 RRF seeds (capped 25 neighbours/seed), contributing **only ids
|
||||
not already in the base legs** — this exclusion is the bug (next bullet).
|
||||
- **Result: the graph was structurally barred from the ranking, so its value is UNMEASURED.** Because
|
||||
graph hits are restricted to ids *outside* the FTS∪dense base set and weighted 0.35, a graph-only id's
|
||||
max RRF (`0.35/61 ≈ 0.0057`) sits below any base-leg id's min (`1.0/110 ≈ 0.0091`) — it can never enter
|
||||
the fused top-k. The "graph ≡ nothing" ablation (§B.6) was therefore **guaranteed by construction, not
|
||||
an empirical finding** (a post-run review found a relevant graph-surfaced memory that fusion discarded).
|
||||
|
||||
### B.5 Fusion (the production recipe, exactly)
|
||||
|
||||
Weighted RRF, `RRF(d) = Σ_leg w_leg/(60 + rank_leg(d))`, chosen over convex combination because it
|
||||
is score-scale-free (no BM25-vs-cosine calibration). Weights `w_fts = 1.0, w_dense = 1.0,
|
||||
w_graph = 0.35`. Each leg pulled to depth 50 before fusion, truncated to k.
|
||||
|
||||
### B.6 Decision-relevant ablation (this is what informs ADR-0004)
|
||||
|
||||
| Config | What | Overall recall@10 | Para recall@10 | Multi recall@10 |
|
||||
|---|---|---|---|---|
|
||||
| **A** full hybrid (FTS+dense+graph) | the prototype | 0.834 | 0.725 | 0.775 |
|
||||
| **B** FTS+dense (w_graph=0) | graph removed | **0.834** | **0.725** | **0.775** |
|
||||
| **C** dense-only | | 0.748 | — | — |
|
||||
| **D** FTS-only (= baseline) | | 0.695 | 0.375 | 0.711 |
|
||||
|
||||
**A and B are identical to three decimals on every metric — but this is a structural artifact (§B.4),
|
||||
not a test of the graph.** The valid signal here is the **FTS-vs-dense decomposition**: dense-only (C)
|
||||
and FTS-only (D) each lose to the fusion (B) — dense recovers paraphrase, lexical recovers exact, fusion
|
||||
gets the best of both. The concept graph itself is **unevaluated** (it could never affect top-k under
|
||||
this fusion config). **This still supports phasing — ship lexical+dense (phase 1), the robust measured
|
||||
win — but the graph is gated pending a *valid* retest, not because it failed.** (Configs B/C/D were not
|
||||
persisted as result JSONs; only A and D are reproducible from committed artifacts.)
|
||||
|
||||
### B.7 Prototype → production mapping
|
||||
|
||||
| Prototype (in-process) | Production (ADR-0002) |
|
||||
|---|---|
|
||||
| numpy cosine over normalized matrix | pgvector `halfvec(1024)` + HNSW ANN |
|
||||
| `.npy` embedding cache, fingerprint-keyed | `embedding` column on `memories`, synced |
|
||||
| in-memory networkx graph (phase 2) | `concepts` / `concept_edges` / `memory_concepts` tables |
|
||||
| `FtsRetriever` (FTS5 in-memory) | existing `search_vector` + GIN (`plainto_tsquery`/`ts_rank`) |
|
||||
| weighted RRF in Python | same RRF (Python handler, or CTE+FULL OUTER JOIN in SQL) |
|
||||
| bge-large local | Voyage-3.5 hosted (non-sensitive) / bge-large local (sensitive, no-key) |
|
||||
|
||||
### B.8 Prototype caveats (carried into the report's limitations)
|
||||
1. **Graph result is INVALID, not merely "null."** The fusion config barred the graph leg from the
|
||||
top-k by construction (§B.4), so the benchmark did not actually test it. A valid retest must include
|
||||
graph candidates in the fused pool (drop the base-set exclusion) and/or sweep the weight, ideally with
|
||||
a typed-relation graph from real LLM extraction and multi-hop queries whose hops are *not* semantically
|
||||
adjacent.
|
||||
2. **Exact-stratum nDCG/MRR dip ~0.018/0.025** vs FTS (recall unaffected) is the standard RRF cost
|
||||
of blending one perfect hit with near-ties; a small exact-match rank bonus could recover it.
|
||||
3. **Latency** (p50 ≈ 230 ms) is CPU-bound on the local query embed; non-gating, GPU/hosted ~10×
|
||||
faster. Baseline FTS was p50 ≈ 15.7 ms (pure SQLite).
|
||||
4. **No pgvector/Postgres** in the prototype — the production substrate is design-only here; the
|
||||
numbers measure *retrieval quality*, which transfers, not the production latency profile.
|
||||
|
||||
---
|
||||
|
||||
## C. Open questions (for production rollout)
|
||||
|
||||
1. **pgvector enablement mechanism** — confirm whether the live CNPG is on the legacy
|
||||
custom-operand-image (likely, since Immich uses pgvector) or the modern image-volume-extensions
|
||||
path; either way the migration is additive, but the enablement DDL/Terraform differs.
|
||||
2. **Graph gate** — what evidence would re-open the concept graph? Candidate: a multi-hop eval slice
|
||||
whose hops are *not* semantically adjacent (where dense can't shortcut), built from real
|
||||
LLM-extracted typed relations rather than keyword co-occurrence. Until then, graph stays off.
|
||||
3. **Voyage vs bge-large in production** — the benchmark ran bge-large (local). A cheap follow-up:
|
||||
re-run the dense leg with Voyage-3.5 on the non-sensitive corpus to confirm the hosted model's
|
||||
higher quality ceiling holds on *our* content before committing the production default.
|
||||
523
docs/research/survey.md
Normal file
523
docs/research/survey.md
Normal file
|
|
@ -0,0 +1,523 @@
|
|||
# Landscape survey: semantic + concept-graph memory for hybrid recall
|
||||
|
||||
Status: research input for the ADR-0001 hybrid upgrade. Scope: how the agent-memory and
|
||||
graph-RAG literature builds and retrieves over a personal-memory store, which embedding
|
||||
model to use, how to fuse lexical + dense + graph signals, and how to evaluate the result.
|
||||
|
||||
**Read this with the decisions already fixed in [ADR-0001](../adr/0001-pursue-hybrid-retrieval-embeddings-and-concept-graph.md)
|
||||
–[0003](../adr/0003-external-embedding-apis-allowed-for-non-sensitive-memories.md):** we pursue
|
||||
hybrid (gated on a benchmark beating FTS), embeddings live in pgvector on the existing CNPG
|
||||
Postgres, the concept graph is node/edge tables in Postgres, sensitive memories never egress,
|
||||
and adoption is decided **quality-first** (recall@k / nDCG@10 / MRR; latency & storage are
|
||||
reported, not gating).
|
||||
|
||||
The recurring conclusion below: **borrow the ideas, not the engines.** None of the four
|
||||
systems surveyed is a drop-in for our stack, but each contributes a mechanism we re-implement
|
||||
natively on Postgres + pgvector.
|
||||
|
||||
---
|
||||
|
||||
## 1. Our workload is the opposite of GraphRAG's design target
|
||||
|
||||
Before comparing systems it helps to state what we are *not*. The graph-RAG family was built
|
||||
for **global sensemaking** ("what are the themes across this corpus") over a **static document
|
||||
collection**. Our workload is the reverse:
|
||||
|
||||
| Dimension | GraphRAG target | claude-memory-mcp |
|
||||
|---|---|---|
|
||||
| Unit | Long documents, chunked | Atomic, already-curated memories (avg ~500 chars) |
|
||||
| Corpus dynamics | Static, indexed once | Append-heavy: a few hundred memories/day arriving |
|
||||
| Query type | Corpus-wide summarization | Point / multi-hop recall ("what did I decide about X") |
|
||||
| Hot path | Offline batch | **Every user prompt** (auto-recall hook fires before each turn) |
|
||||
| Scale | 10k–1M+ chunks | ~5k memories today → tens of thousands |
|
||||
|
||||
This mismatch is the lens for everything that follows. The expensive part of GraphRAG —
|
||||
community detection + hierarchical LLM summaries — is the *wrong retrieval unit* for atomic
|
||||
point lookups, and re-summarizing communities on a sustained append stream is its dominant,
|
||||
unbounded cost. We want a design whose index-time work is **proportional to new content**, and
|
||||
whose retrieval path has **no LLM call** (so it fits the per-prompt budget).
|
||||
|
||||
---
|
||||
|
||||
## 2. The GraphRAG family — Microsoft GraphRAG, LightRAG, nano-graphrag, LazyGraphRAG
|
||||
|
||||
All four turn text into an entity–relation knowledge graph via LLM extraction; they differ on
|
||||
the expensive part (community detection + hierarchical summarization), which is exactly where
|
||||
**incremental cost** lives.
|
||||
|
||||
### Microsoft GraphRAG (Edge et al. 2024, arXiv 2404.16130)
|
||||
Pipeline: chunk → LLM extracts entities + relationships per chunk (with multi-round
|
||||
"gleanings" to catch misses) → summarize duplicate element instances into node/edge
|
||||
descriptions → build graph → **Leiden** community detection producing a *hierarchy*
|
||||
(levels C0..C3) → an LLM writes a **community report** for every community at every level.
|
||||
Two query modes: **global** (map-reduce over all community reports — corpus-wide
|
||||
sensemaking) and **local** (start from query-relevant entities, fan out). Indexing is
|
||||
LLM-heavy: ~4,000 LLM calls / ~35 min for one textbook; ~$20–40 per 1M tokens with gpt-4o.
|
||||
|
||||
**Incremental:** the `graphrag update` command (GraphRAG 1.0) computes deltas and places new
|
||||
entities into existing communities "rather than re-running Leiden," re-summarizing only
|
||||
changed communities — **but** maintainers warn that once drift crosses a threshold "the worst
|
||||
case degrades to the same performance as a normal indexing." A periodic, unpredictable
|
||||
full-reindex cliff on a sustained append stream. Parquet/file-pipeline oriented, not
|
||||
Postgres-native.
|
||||
|
||||
### LightRAG (HKUDS, arXiv 2410.05779, EMNLP 2025)
|
||||
Pipeline: chunk → LLM extracts entities + relations → "profiling" generates a key-value text
|
||||
summary per node/edge → **deduplicate** merges identical entities/relations across chunks.
|
||||
**No community detection.** Retrieval is **dual-level**: the LLM splits the query into
|
||||
low-level keywords (specific entities) and high-level keywords (broad themes via relationship
|
||||
chains); each set is matched by *vector* similarity against an entity-vector index and a
|
||||
relation-vector index, then one-hop neighbours are pulled from the graph. Modes:
|
||||
naive / local / global / hybrid / mix (mix = default).
|
||||
|
||||
**Incremental (the crux):** a new document goes through the same local indexing to produce a
|
||||
small local graph, then is integrated by **set union** of node-sets and edge-sets into the
|
||||
existing graph — "eliminating the need to rebuild the entire index graph." No communities ⇒
|
||||
**no global re-clustering or re-summarization, ever** ⇒ O(new content) per insert, the only
|
||||
genuinely-incremental member. Ships a PostgreSQL all-in-one backend (PGVectorStorage on
|
||||
pgvector + PGGraphStorage on Apache AGE + KV + doc-status in one DB, PG ≥16.6).
|
||||
|
||||
### nano-graphrag (~1100 LOC)
|
||||
A faithful minimal reimplementation of Microsoft GraphRAG and an excellent compact *reference*
|
||||
for the exact extraction/community/report prompts. **Hard NO for incremental:** README states
|
||||
plainly "each time you insert, the communities of graph will be re-computed and the community
|
||||
reports will be re-generated" — O(whole graph) LLM cost per append.
|
||||
|
||||
### LazyGraphRAG (Microsoft Research, 2024)
|
||||
Defers **all** LLM work to query time: index time uses only NLP noun-phrase extraction + graph
|
||||
statistics — "indexing costs are identical to vector RAG and 0.1% of the costs of full
|
||||
GraphRAG." Sidesteps the incremental-re-summarization problem entirely by never pre-summarizing
|
||||
communities. The **defer-LLM-cost principle** is the one to borrow.
|
||||
|
||||
### Verdict for us
|
||||
**Adopt none wholesale; steal LightRAG's architecture + LazyGraphRAG's defer-LLM principle.**
|
||||
LightRAG is the only one whose incremental model (pure set-union, no re-clustering) structurally
|
||||
fits an append-heavy stream, and whose retrieval path (vector + one-hop graph, no query-time
|
||||
map-reduce) is hot-path-viable. But adopting LightRAG-the-product is not recommended: its
|
||||
Postgres graph path needs the **Apache AGE** extension (not on our CNPG), and that path has
|
||||
documented concurrency/entity-merge instability under append-heavy load (asyncpg pool timeouts
|
||||
at the merge stage; slow upgrades). Our multi-hop is shallow (1–2 hops), expressible in plain
|
||||
recursive SQL CTEs over node/edge tables — no AGE needed.
|
||||
|
||||
---
|
||||
|
||||
## 3. Zep / Graphiti — temporal knowledge graph for agent memory
|
||||
|
||||
Zep (arXiv 2501.13956) is an agent-memory service; **Graphiti** is its open engine
|
||||
(Neo4j / FalkorDB / Kuzu backend, ~20k stars, MIT). It is the **closest conceptual analog** to
|
||||
our hybrid goal — it fuses exactly the three signals ADR-0001 wants.
|
||||
|
||||
**Three-tier graph:** episode subgraph (raw ingested data, the provenance layer ≈ our Memory
|
||||
rows) → semantic entity subgraph (entity nodes + typed relationship edges, each linking back to
|
||||
its source episodes) → community subgraph (clusters with LLM summaries — the GraphRAG "global"
|
||||
layer).
|
||||
|
||||
**Bi-temporal model:** every semantic edge carries **four** timestamps on two timelines —
|
||||
*valid time* (`t_valid`/`t_invalid`: when the fact held true in the world) and *transaction
|
||||
time* (`t'_created`/`t'_expired`: when Zep learned/retracted it). Facts are never deleted;
|
||||
superseded facts get their validity window closed. This is a principled, queryable version of
|
||||
our **"supersede, don't accumulate"** memory discipline.
|
||||
|
||||
**Incremental ingestion (per episode):** a *sequence* of LLM calls — entity extraction →
|
||||
entity resolution/dedup (embed + cosine + BM25 search against existing nodes, then an LLM
|
||||
judges merge vs create) → fact (edge) extraction → fact dedup → temporal extraction (resolve
|
||||
"two weeks ago" against a reference time) → edge invalidation (LLM compares each new edge
|
||||
against related existing edges; on a temporally-overlapping contradiction it closes the old
|
||||
edge). Cost is **heavy on write**, paid back on reads.
|
||||
|
||||
**Retrieval (sub-second, NO LLM at query time):** three parallel searches fused, then reranked.
|
||||
- `φ_cos`: cosine over embeddings of fact text / entity names / community summaries (BGE-m3, 1024-d).
|
||||
- `φ_bm25`: BM25 full-text over the same fields.
|
||||
- `φ_bfs`: breadth-first n-hop graph traversal from seed nodes — the genuinely graph-native signal.
|
||||
- Rerank: pluggable — **RRF**, MMR (diversity), episode-mentions (frequency), node-distance, or a cross-encoder (most accurate, slowest).
|
||||
|
||||
**Published quality (the strongest evidence in this family):** on **LongMemEval**, Zep reports
|
||||
**+18.5%** accuracy over a baseline (71.2% vs 60.2% with gpt-4o) *and* ~90% query-latency
|
||||
reduction; on **MemGPT DMR**, 94.8% vs 93.4%. These are conversational long-context QA, not
|
||||
personal-fact recall@k — so the headline numbers won't transfer directly, but the *fusion
|
||||
recipe* is exactly what we benchmark.
|
||||
|
||||
### Verdict for us
|
||||
**Primary design blueprint for the concept-graph half — but not an adopted dependency.**
|
||||
Graphiti has **no pgvector backend**; adopting the engine forces a new Neo4j/FalkorDB graph DB
|
||||
into the cluster, conflicting with ADR-0002 and reuse-before-building. We borrow four mechanisms,
|
||||
re-implemented on Postgres: (1) the episodic(=Memory rows)/semantic(=new node+edge tables)
|
||||
split; (2) the parallel-search + RRF fusion read path; (3) resolution-via-search to dedupe the
|
||||
graph using our existing FTS+vector; (4) bi-temporal edge invalidation as the queryable form of
|
||||
our supersede discipline. We de-scope the community/summarization tier and the default
|
||||
cross-encoder. Two hard caveats: keep the multi-LLM-call extraction **off the hot path**
|
||||
(background, like our sync engine), and route extraction through in-cluster llama-cpp / filter
|
||||
`is_sensitive` per ADR-0003.
|
||||
|
||||
---
|
||||
|
||||
## 4. Mem0 / Mem0g — extraction-based, LLM-curated memory
|
||||
|
||||
Mem0 (arXiv 2504.19413) is a **write-side memory curator**, not a retrieval algorithm — it
|
||||
solves a *different axis* than our gated problem, and is **complementary**.
|
||||
|
||||
**Two-phase pipeline.** *Extraction:* on each new message pair, an LLM (fed an async
|
||||
conversation summary + the last ~10 messages) emits a set of concise "candidate facts."
|
||||
*Update (the curation step):* for each candidate fact, retrieve the top ~10 semantically-similar
|
||||
existing memories, then a function-calling LLM picks one of four ops — **ADD** (new), **UPDATE**
|
||||
(merge richer detail into an existing id, gated on information content), **DELETE** (a
|
||||
contradicted memory), **NOOP**. Net effect: the store self-deduplicates, self-merges, and
|
||||
self-supersedes instead of accumulating. Two LLM calls per write (extract + decide) + a vector
|
||||
search; **async by default** (off the user hot path); the **read/search path is pure vector
|
||||
similarity with no LLM**.
|
||||
|
||||
**Mem0g (graph variant):** a directed labeled entity graph (Alice –lives_in→ SF) on Neo4j; a
|
||||
conflict-detection + LLM update-resolver marks superseded relationships *invalid* rather than
|
||||
deleting them.
|
||||
|
||||
**Published quality:** on **LOCOMO**, Mem0 J=66.88 / Mem0g 68.44 beats OpenAI memory (52.90),
|
||||
A-Mem (48.38), LangMem (58.10), ties Zep (65.99), at ~1/15th the tokens of full-context; Mem0g
|
||||
specifically wins temporal reasoning. Reference latencies (gpt-4o-mini): search p95 ≈ 0.20s,
|
||||
total p95 ≈ 1.44s, vs full-context ≈ 17s.
|
||||
|
||||
### Verdict for us
|
||||
**Adopt the curation loop as a separate, flagged subsystem — it does NOT move the ADR-0001
|
||||
retrieval metric by itself** (its search is vector-only, no lexical+graph fusion). The
|
||||
ADD/UPDATE/DELETE/NOOP loop is the highest-leverage idea Mem0 offers: it automates a discipline
|
||||
our own rules already mandate (every correction stored, supersede-don't-accumulate, tombstones)
|
||||
but currently leave to manual human effort. It is cheap to build against our existing Memory
|
||||
model + `update_memory` endpoint, runs async off the recall hot path, and respects the
|
||||
`is_sensitive` boundary. **Hard guardrails required:** never physically DELETE — supersede to a
|
||||
`[SUPERSEDED]` tombstone (importance ~0.3, per our convention); log every op; gate behind the
|
||||
non-sensitive filter. Keep extraction *optional* (our memories are already atomic, so usually
|
||||
only the single UPDATE-decision call is needed). Mem0g's "mark invalid, not delete" and triplet
|
||||
schema (source, relation, dest) are reusable ideas, but implemented on pgvector/Postgres, not
|
||||
Neo4j. **Critically: isolate curation behind a flag so the benchmark measures retrieval quality
|
||||
independently of any curation behaviour change.**
|
||||
|
||||
---
|
||||
|
||||
## 5. HippoRAG / HippoRAG 2 — Personalized PageRank over a concept graph
|
||||
|
||||
HippoRAG (NeurIPS 2024, arXiv 2405.14831) and HippoRAG 2 (ICML 2025, arXiv 2502.14802) are the
|
||||
**strongest published evidence that a concept graph wins on multi-hop** — precisely the query
|
||||
class ADR-0001 says the graph must beat lexical on.
|
||||
|
||||
**Mechanism (hippocampal indexing analogy):** LLM = neocortex; retrieval encoder =
|
||||
parahippocampal region (detects synonyms); open KG = hippocampal index. *Offline:* an LLM runs
|
||||
OpenIE on each passage → a schema-free KG of noun-phrase nodes joined by relation edges; the
|
||||
encoder adds **synonym edges** between phrase nodes with cosine > τ=0.8. *Online, per query:*
|
||||
(1) ONE LLM call does NER on the query; (2) the encoder links query entities to nearest KG
|
||||
nodes = **seed nodes**; (3) each seed weight is scaled by node specificity (`|P_i|⁻¹`, an
|
||||
IDF-like rare-phrase boost) and written into the **Personalized PageRank** reset vector;
|
||||
(4) PPR runs to convergence (damping 0.5); (5) the phrase-node probability vector scores
|
||||
passages. Multi-hop emerges because the random walk reaches passages sharing **no** query tokens
|
||||
— in **one** retrieval step instead of iterative retrieve-reason loops.
|
||||
|
||||
**HippoRAG 2** makes passages first-class nodes (linked to their phrases by "contains" context
|
||||
edges), shifts linking to query→triple + **LLM triple-filtering** ("recognition memory"), and
|
||||
seeds *all* passage nodes by embedding similarity (small weight ~0.05) so dense and graph blend
|
||||
in one PPR. Net effect: a single PPR fuses lexical-ish phrase matching, dense passage
|
||||
similarity, and multi-hop traversal into one ranked list.
|
||||
|
||||
**Published quality (passage recall@5):** HippoRAG 2 beats the strongest 7B embedding baseline
|
||||
(NV-Embed-v2) on every multi-hop set — 2Wiki **90.4 vs 76.5** (+13.9), MuSiQue **74.7 vs 69.7**
|
||||
(+5.0), HotpotQA **96.3 vs 94.5** — and is the only structure-augmented method that *doesn't
|
||||
regress* simple QA (NQ 78.0).
|
||||
|
||||
### Verdict for us
|
||||
**Adopt the idea (PPR spreading activation over our concept graph), not the framework.** Two
|
||||
hard adaptations, both fitting our stack:
|
||||
1. **Drop the per-query LLM** (v1 NER / v2 triple-filtering) — the only thing that would blow
|
||||
the hot-path budget — and **seed PPR from our existing FTS top-k ∪ pgvector top-k**, weighted
|
||||
by fused score × importance × node-specificity. This turns PPR into the *fusion layer*
|
||||
ADR-0001 wants, with zero added LLM latency.
|
||||
2. **Prefer a memory-node graph** (memories as nodes, our typed Relationships as edges) over
|
||||
HippoRAG's phrase explosion (it turns 11.6k passages into ~92k nodes; at our scale that'd be
|
||||
~43k phrase nodes). Leaner and native to ADR-0002's node/edge tables.
|
||||
|
||||
A reproducible PPR latency micro-benchmark on a 5,400-memory graph measured **~2 ms** (memory-node
|
||||
graph, transition matrix cached) to **~21 ms** (full phrase graph), ~105 ms even at 3× growth —
|
||||
PPR is **not** the bottleneck; the stock recipe's online LLM is (which we remove). Postgres can
|
||||
*store* the graph but has no native PPR (pgrouting = shortest-path only), so PPR is computed in
|
||||
Python over a cached `scipy.sparse` transition matrix loaded from the node/edge tables, rebuilt
|
||||
only on graph mutation. **Caveat for the gate:** our LLM-free seeding variant is *not* validated
|
||||
by the papers, and our 5.4k personal corpus is far smaller and less multi-hop-dense than their
|
||||
90k-node Wikipedia graphs — so the benchmark must confirm the multi-hop win transfers.
|
||||
|
||||
---
|
||||
|
||||
## 6. Embedding model survey
|
||||
|
||||
Our `content` and `expanded_keywords` are **short** prose (capped ~500 chars), so a model's
|
||||
max-token limit is effectively a non-constraint — quality, dimensionality, and deploy
|
||||
feasibility decide.
|
||||
|
||||
### Self-hostable (sentence-transformers on the GPU node, or GGUF via llama-cpp; pgvector stores the vector)
|
||||
|
||||
| Model | Dim | Params / VRAM (fp16) | MTEB(en) avg | License |
|
||||
|---|---|---|---|---|
|
||||
| nomic-embed-text-v1.5 | 768 (Matryoshka 64–768) | 0.1B / <1 GB | 62.28 | Apache-2.0 |
|
||||
| bge-base-en-v1.5 | 768 | 109M / ~0.5 GB | ~63.5 | MIT |
|
||||
| **bge-large-en-v1.5** | **1024** | 335M / ~1.3 GB | **64.23** | MIT |
|
||||
| e5-large-v2 | 1024 | 0.3B / ~1.3 GB | ~62.25 | MIT |
|
||||
| bge-m3 | 1024 dense (+sparse +ColBERT) | 568M / ~1–2.4 GB | en ~59–60 (strong multiling/BEIR) | MIT |
|
||||
| gte-Qwen2-1.5B-instruct | 1536 | 1.5B / ~3.4 GB | **67.16** (top of set) | Apache-2.0 |
|
||||
|
||||
### Hosted (API call, NON-SENSITIVE memories only per ADR-0003)
|
||||
|
||||
| Model | Dim | MTEB(en) avg | Price /1M tok | License |
|
||||
|---|---|---|---|---|
|
||||
| OpenAI text-embedding-3-small | 1536 (Matryoshka→256) | 62.3 | $0.02 | proprietary |
|
||||
| OpenAI text-embedding-3-large | 3072 (Matryoshka) | 64.6 | $0.13 | proprietary |
|
||||
| **Voyage-3.5** | **1024** (+256/512/2048, int8/binary) | beats OpenAI-3-large ~7.5% on Voyage's eval | $0.06 (first 200M free) | proprietary |
|
||||
| Voyage-3.5-lite | 1024 | beats OpenAI-3-large ~2–3.8% | $0.02–0.03 | proprietary |
|
||||
| Cohere embed-english-v3.0 | 1024 (native int8/binary) | ~64.5 | ~$0.10 (sales-quoted) | proprietary |
|
||||
|
||||
**Implementation notes that matter.** Use **asymmetric** prompting (query vs document):
|
||||
sentence-transformers `encode_query`/`encode_document`, always `normalize_embeddings=True` so
|
||||
pgvector cosine == dot product. e5-large-v2 *requires* manual `"query: "`/`"passage: "` prefixes
|
||||
or quality collapses; bge prepends a query instruction; gte-Qwen2 prepends a task instruction to
|
||||
queries only. Pick the dimension **once** — changing it later forces a full re-embed + HNSW
|
||||
rebuild.
|
||||
|
||||
### Recommendation (one of each, quality-first)
|
||||
- **Local: BAAI/bge-large-en-v1.5** (1024-d, MIT) — best quality-per-complexity in the
|
||||
self-hostable set for short English memories: strong retrieval, ~1.3 GB VRAM (runs on CPU at
|
||||
~100 ms), no `trust_remote_code`, mature ST support. The 512-token cap is irrelevant for our
|
||||
content. (gte-Qwen2-1.5B-instruct is the explicit upgrade candidate if the benchmark says
|
||||
bge-large leaves quality on the table; nomic is the fallback if a long context or sub-768
|
||||
Matryoshka dims are ever wanted.)
|
||||
- **Hosted: Voyage-3.5** (1024-d) — highest measured retrieval quality of the hosted options,
|
||||
**same 1024-d as the local pick** so the pgvector column and fusion code are identical whether
|
||||
local or hosted (clean A/B), and our whole corpus embeds inside the free tier. Non-sensitive
|
||||
only; sensitive rows go to bge-large locally. (OpenAI text-embedding-3-small is the pragmatic
|
||||
fallback if no Voyage key.)
|
||||
|
||||
> **Prototype note:** the prototype as built used **bge-large-en-v1.5** (1024-d, local default,
|
||||
> no API key in env). Production should adopt **Voyage-3.5** (also 1024-d) for non-sensitive
|
||||
> memories per ADR-0003, keeping bge-large as the sensitive-only / no-key fallback. Both 1024-d
|
||||
> means the pgvector schema and fusion code are unchanged across the choice.
|
||||
|
||||
---
|
||||
|
||||
## 7. Fusion of lexical + dense + graph signals
|
||||
|
||||
Three retrieval families produce candidate lists per recall; one fusion function merges them.
|
||||
|
||||
### Reciprocal Rank Fusion (RRF) — rank-based, scale-agnostic
|
||||
Cormack/Clarke/Büttcher (SIGIR'09): `score(d) = Σ_s 1/(k + rank_s(d))`, summed over every signal
|
||||
the doc appears in. `k` is a smoothing constant — their sweep found **k=60 near-optimal but
|
||||
uncritical** (≤0.3% MAP swing across [10,100]; k=0 or k=500 costs 3–4%). A doc present in one
|
||||
list but absent from another contributes `1/(k+rank)` where it fires and **0** elsewhere — so
|
||||
multi-signal agreement is rewarded and single-signal hits are penalized (the hybrid behaviour we
|
||||
want). Extends to N lists trivially (just sum), which makes a 3-way fuse a one-liner.
|
||||
**Weighted RRF:** `Σ_s w_s/(k + rank_s(d))` — bias a stronger signal, no pre-normalization.
|
||||
|
||||
### Weighted score fusion / convex combination (CC) — score-based, needs normalization
|
||||
Bruch et al. (arXiv 2210.11934, TOIS 2023): `f = α·φ(semantic) + (1-α)·φ(lexical)` with
|
||||
**theoretical** min-max normalization (TM2C2): cosine min = -1, BM25 min = 0, per-query max —
|
||||
stable across queries. Findings: **CC/TM2C2 beats RRF on nDCG and recall** in- and out-of-domain
|
||||
(RRF ~3.86% lower nDCG@10 in one replication); Weaviate switched its default from rankedFusion to
|
||||
relativeScoreFusion (min-max CC) for ~6% recall on FIQA. CC is sample-efficient (α tunes from a
|
||||
small labeled set) but requires calibratable scores.
|
||||
|
||||
### Folding in graph hits
|
||||
Build a graph candidate list, then feed it to the fuser as just another ranked list:
|
||||
**seed** (match the query to concept nodes via the same FTS + dense over node labels) →
|
||||
**traverse** (1–2 hops to reachable memories) → **score** each reachable memory. Three documented
|
||||
scorings: hop-decay `Σ_paths β^hops` (β≈0.5–0.7); Personalized PageRank seeded on matched nodes
|
||||
(HippoRAG); or node-degree priority (GraphRAG local search). The
|
||||
*Calibrated-Fusion-for-Graph-Vector* paper (arXiv 2603.28886) is explicit: naive graph+vector
|
||||
fusion fails on **scale incompatibility**, so convert graph traversal into a probability-like
|
||||
normalized score before fusing. Crucial consequence: the graph list is **sparse** (often a
|
||||
handful of memories, sometimes zero). Under RRF that's handled automatically; under CC you must
|
||||
explicitly treat "absent" as the theoretical min or the missing-modality term silently biases the
|
||||
sum.
|
||||
|
||||
### Cross-encoder re-rank — a separate stage-2, not a fusion function
|
||||
Retrieve top-N each → fuse → take fused top ~20–30 → score each (query, memory) pair jointly with
|
||||
a cross-encoder (e.g. bge-reranker-v2-m3) → re-sort. Reported lift +5 to +15 nDCG@10 on
|
||||
BEIR/MTEB; cost scales with pair count so it is only ever a small-candidate-set stage.
|
||||
|
||||
### Recommendation
|
||||
**Weighted RRF over three lists (FTS, dense, graph), k=60, equal weights to start**, with
|
||||
importance applied as a deterministic post-fusion prior and a cross-encoder as an optional,
|
||||
benchmark-gated stage-2. RRF is the right *default* because we fuse three incompatible scales,
|
||||
one of them sparse/often-empty; it is near-parameter-free; and it collapses to exactly today's
|
||||
lexical ordering when dense/graph are empty (the SQLite graceful-degrade path). **But because
|
||||
adoption is quality-gated, the benchmark must also run CC/TM2C2 as a challenger** — the
|
||||
literature is consistent that CC edges RRF on quality when scores are calibratable. (See the
|
||||
[benchmark report](benchmark-report.md) for which won on *our* eval set.)
|
||||
|
||||
---
|
||||
|
||||
## 8. Concept-graph construction from memories
|
||||
|
||||
Turning flat Memory rows into nodes (concepts/entities) + typed directed edges + memory→concept
|
||||
"mentions" edges. Three extraction families:
|
||||
|
||||
- **(A) Open LLM triple extraction** (schema-free) — prompt an LLM to emit `[subject, relation,
|
||||
object]` triples. High recall, but relation labels proliferate ("prefers"/"likes"/"favors"), so
|
||||
it **requires** downstream canonicalization. GraphRAG is the canonical implementation
|
||||
(extract + gleaning + cross-chunk entity summarization).
|
||||
- **(B) Schema-guided** — constrain to a fixed ontology. Cleaner, but a fixed schema misses
|
||||
surprises in a heterogeneous personal corpus. **EDC** (Zhang & Soh, EMNLP 2024) bridges the two:
|
||||
*extract* open triples → *define* (LLM writes a one-sentence definition per distinct relation) →
|
||||
*canonicalize* (embed definitions, retrieve nearest existing relations, LLM verifies map-vs-add).
|
||||
Two modes: target-alignment (fixed schema) and self-canonicalization (grow schema dynamically).
|
||||
- **(C) Entity resolution / canonicalization** (the dedup problem — "Svelte"/"SvelteKit"/"svelte
|
||||
framework" are one node): cluster-then-refine on the *aggregated* graph — embed every surface
|
||||
string, cluster by cosine (HDBSCAN / connected-components over a threshold), optional
|
||||
LLM-as-judge per cluster. KGGEN (arXiv 2502.09956) does iterative LLM-guided clustering;
|
||||
Graphiti uses MinHash+LSH fast-path with LLM fallback. **Cost scales with distinct entities (low
|
||||
thousands), not with memory count.**
|
||||
- **(D) Lightweight non-LLM** — spaCy NER + noun-chunks + co-occurrence edges, or **ReLiK**
|
||||
(Sapienza, ACL 2024) for *typed* relations on CPU at up to 40× LLM speed, zero per-doc LLM cost.
|
||||
The natural ablation baseline and sqlite-only fallback.
|
||||
|
||||
**The tractable recipe for our corpus.** Measured: 5,452 non-sensitive memories ≈ 683K content
|
||||
tokens total — *tiny*. At ~125 content-tokens/memory, ~570 memories pack into one 100K-token
|
||||
request, so the **entire corpus extracts in ~10–25 batched LLM calls, not 5,452 sequential
|
||||
calls**. Pipeline: (1) batch-extract open triples (each memory tagged with its `memory_id` so
|
||||
triples map back), parallelized — LangExtract / KGGEN style; (2) aggregate + canonicalize globally
|
||||
*once* (embed distinct entities, cluster, LLM-judge only ambiguous clusters — tens of calls);
|
||||
(3) optionally one batched LLM "define relations" pass for EDC-style relation canonicalization.
|
||||
Total budget: low tens of calls, minutes of wall-clock, a few dollars hosted or one GPU-node
|
||||
llama-cpp session. **Canonicalization quality (the similarity threshold / cluster granularity) is
|
||||
where this lives or dies and must be tuned against held-out data, not eyeballed.** Write-time /
|
||||
Graphiti-style per-memory extraction is for *incremental updates only* — the wrong tool for the
|
||||
one-shot backfill.
|
||||
|
||||
---
|
||||
|
||||
## 9. Vector storage in Postgres (production substrate)
|
||||
|
||||
`pgvector` is a **proven capability on our exact CNPG cluster** (Immich already does vector search
|
||||
there, and claude-memory-mcp is already a tenant of the shared `pg-cluster-rw.dbaas` behind
|
||||
PgBouncer) — zero new infrastructure, reuse-before-building satisfied.
|
||||
|
||||
- **HNSW** (recommended default): `USING hnsw (embedding halfvec_cosine_ops) WITH (m=16,
|
||||
ef_construction=64)`; query knob `SET hnsw.ef_search` (default 40). Best speed-recall tradeoff;
|
||||
buildable on an empty table; graph in RAM. **IVFFlat** is rejected (must be built *after* data
|
||||
exists — an empty-table footgun — and has a lower recall ceiling).
|
||||
- **halfvec** (fp16, 2 bytes/dim) halves index size at ~no recall loss; indexable ≤4000 dims.
|
||||
768-d halfvec = 1536 bytes/row; at our scale total embedding storage is single-digit MB.
|
||||
- **Filtered ANN:** we always filter `deleted_at IS NULL` (often `category`). Post-filtering can
|
||||
under-fill top-k; enable `hnsw.iterative_scan='relaxed_order'`, and **always add a tie-breaker**
|
||||
(`, id`) since approximate indexes give non-deterministic order.
|
||||
- **Hybrid in one query:** each retriever is a CTE producing a per-ranker rank; fuse with RRF via
|
||||
FULL OUTER JOIN on memory id — no score calibration needed across the incomparable ts_rank and
|
||||
cosine scales.
|
||||
- **pgvectorscale / StreamingDiskANN** (bounded-RAM disk graph, SBQ compression) is **deferred** —
|
||||
Rust/pgrx must be compiled into the operand image, and it only earns its keep above ~1–5M
|
||||
vectors. Our corpus is orders of magnitude below that.
|
||||
- **PgBouncer gotcha:** per-query GUCs (`hnsw.ef_search`) must be `SET LOCAL` inside the recall
|
||||
transaction, not session-level, under transaction pooling.
|
||||
|
||||
**Not for the prototype** (the prototype uses an in-process numpy index); this is the production
|
||||
adoption path *if* the benchmark clears the gate — an additive Alembic migration (one nullable
|
||||
`halfvec(1024)` column + HNSW index) plus a Terraform change to the CNPG stack.
|
||||
|
||||
---
|
||||
|
||||
## 10. Evaluation methodology
|
||||
|
||||
A retrieval test collection = corpus + query set + **qrels** (relevance judgments). For each
|
||||
query, call recall, take the ordered list of returned memory ids, score against qrels — measuring
|
||||
the *retriever in isolation*, exactly what ADR-0001's gate needs.
|
||||
|
||||
**Metrics (compute all; pick one primary):**
|
||||
- **Recall@k** — "did we surface the right memory at all?" *The* hot-path metric (auto-recall
|
||||
injects top-N; if the memory isn't in top-k it can't help). Report @5/@10/@20/@30.
|
||||
- **nDCG@k** — graded + position-aware; the best single summary (BEIR standard is nDCG@10).
|
||||
Headline quality number for the gate.
|
||||
- **MRR** — only the first hit matters; relevant for the exact-lookup stratum.
|
||||
- **MAP** — broad binary recall+precision blend; secondary, stable for significance tests.
|
||||
|
||||
**Stratification (the ADR-0001 hypothesis-targeted design):** *exact/lexical* (FTS already wins —
|
||||
the **regression guard**); *paraphrase/semantic* (disjoint vocabulary — the value-of-embeddings
|
||||
test); *multi-hop* (≥2 memories or a concept link — the graph test).
|
||||
|
||||
**Qrels generation (the LongMemEval pipeline, inverted for memories):** sample seed memories
|
||||
stratified by category + importance → an LLM generates exact / paraphrase / multi-hop queries →
|
||||
label relevant ids, with **pooling** (union the top-k of every arm, TREC-style) and an LLM-judge
|
||||
on the **UMBRELA 0–3 scale**. **Separate the generator model from the judge model** to avoid
|
||||
self-preference leakage. **Hand-verify** a ≥15–20% sample (oversample multi-hop) and require
|
||||
Cohen's κ(LLM, human) ≥ ~0.6 before trusting auto-labels; always hand-author multi-hop
|
||||
relevant-id sets.
|
||||
|
||||
**Pitfalls with standard mitigations (all baked into the protocol):** LLM judges are
|
||||
systematically *lenient* (κ gate); "holes" (new arms retrieve unjudged docs — must pool *all*
|
||||
arms before judging, else the gate is rigged against semantic/hybrid); generator-as-judge leakage
|
||||
(model separation); too-easy self-generated queries (check paraphrase shares no content tokens);
|
||||
adversarial/unanswerable queries have no relevant id and **must be kept out of the ranked metrics**
|
||||
(mixing them corrupted the disputed Zep-vs-mem0 LOCOMO comparison — 84%→58%).
|
||||
|
||||
**Sizing:** Voorhees & Buckley (2002) — ≥25 topics is the floor, 50 yield reliable rankings, and a
|
||||
~5–6% absolute gap at n=50 is needed for 95% confidence the ordering holds on a different query
|
||||
set. Since the gate is *per stratum*, each stratum wants its own ~50 queries.
|
||||
|
||||
> **Honest note on what we actually built:** our eval set is **119 queries (40 exact / 40
|
||||
> paraphrase / 39 multihop)** — just below the ~50/stratum ideal, and qrels were LLM-generated
|
||||
> with lighter hand-verification than the full protocol prescribes. This is a real limitation,
|
||||
> tracked in the [benchmark report](benchmark-report.md).
|
||||
|
||||
---
|
||||
|
||||
## 11. Synthesis — what we borrow, from whom
|
||||
|
||||
| Source | Borrowed mechanism | Re-implemented as | Adopted? |
|
||||
|---|---|---|---|
|
||||
| LightRAG | Incremental set-union graph merge; dual-level retrieve | Native node/edge tables, no AGE; FTS+dense+graph fuse | Idea only |
|
||||
| LazyGraphRAG | Defer LLM cost; index-time work ∝ new content | Store-time extraction off hot path | Principle |
|
||||
| Zep / Graphiti | Episodic/semantic split; 3-signal RRF read path; bi-temporal invalidation | Memory rows + Postgres node/edge tables; pgvector+FTS+CTE | **Blueprint** |
|
||||
| Mem0 | ADD/UPDATE/DELETE/NOOP write-side curation | Flagged async curator over existing `update_memory` | Complementary, flagged |
|
||||
| HippoRAG 2 | PPR spreading activation for multi-hop | LLM-free FTS+vector-seeded PPR over memory-node graph (phase 2, gated) | Idea only |
|
||||
| Bruch et al. / Cormack | RRF default + CC/TM2C2 challenger | Weighted RRF k=60, post-fusion importance prior | **Direct** |
|
||||
| EDC / KGGEN | Open-extract → define → canonicalize globally | Batched extraction + embedding-cluster canonicalization | **Direct** |
|
||||
| pgvector / Supabase | HNSW + halfvec + RRF hybrid in one SQL query | Additive migration to CNPG (production only) | **Production design** |
|
||||
| LongMemEval / UMBRELA / Voorhees | Stratified LLM-qrels + pooling + κ gate | Our exact/paraphrase/multi-hop eval | **Direct** |
|
||||
|
||||
The through-line: **a memory-node concept graph, dense pgvector embeddings, and the existing
|
||||
lexical FTS, fused with weighted RRF, with all LLM work pushed to store time** — sized for an
|
||||
append-heavy personal store and gated on a benchmark that beats FTS.
|
||||
|
||||
---
|
||||
|
||||
## Sources
|
||||
|
||||
**GraphRAG family**
|
||||
- Edge et al., "From Local to Global: A Graph RAG Approach…" (Microsoft, 2024) — arXiv 2404.16130
|
||||
- Microsoft GraphRAG incremental-indexing design — github.com/microsoft/graphrag/issues/741; GraphRAG 1.0 blog (microsoft.com/en-us/research/blog/moving-to-graphrag-1-0…)
|
||||
- Guo et al., "LightRAG: Simple and Fast RAG" (HKUDS, EMNLP 2025) — arXiv 2410.05779; github.com/HKUDS/LightRAG (incl. issue #2122, PG+AGE concurrency)
|
||||
- nano-graphrag — github.com/gusye1234/nano-graphrag
|
||||
- LazyGraphRAG — microsoft.com/en-us/research/blog/lazygraphrag-setting-a-new-standard-for-quality-and-cost/
|
||||
|
||||
**Temporal KG memory**
|
||||
- Rasmussen et al., "Zep: A Temporal Knowledge Graph Architecture for Agent Memory" (2025) — arXiv 2501.13956
|
||||
- Graphiti — github.com/getzep/graphiti; Neo4j writeup (neo4j.com/blog/developer/graphiti-knowledge-graph-memory/)
|
||||
|
||||
**Extraction-based memory**
|
||||
- "Mem0: Building Production-Ready AI Agents…" (2025) — arXiv 2504.19413; github.com/mem0ai/mem0 (configs/prompts.py)
|
||||
|
||||
**Graph-PPR retrieval**
|
||||
- Gutiérrez et al., "HippoRAG" (NeurIPS 2024) — arXiv 2405.14831
|
||||
- "From RAG to Memory" = HippoRAG 2 (ICML 2025) — arXiv 2502.14802; github.com/OSU-NLP-Group/HippoRAG
|
||||
|
||||
**Embeddings**
|
||||
- bge-large-en-v1.5 — huggingface.co/BAAI/bge-large-en-v1.5; gte-Qwen2-1.5B — huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct; nomic — huggingface.co/nomic-ai/nomic-embed-text-v1.5; bge-m3 — huggingface.co/BAAI/bge-m3; e5-large-v2 — huggingface.co/intfloat/e5-large-v2
|
||||
- Voyage-3/3.5 — blog.voyageai.com/2024/09/18/voyage-3/; docs.voyageai.com/docs/pricing
|
||||
- OpenAI text-embedding-3 — developers.openai.com/api/docs/guides/embeddings; Cohere embed v3 — docs.cohere.com/docs/cohere-embed
|
||||
|
||||
**Fusion**
|
||||
- Cormack, Clarke, Büttcher (SIGIR'09) — cormack.uwaterloo.ca/cormacksigir09-rrf.pdf
|
||||
- Bruch et al., "An Analysis of Fusion Functions for Hybrid Retrieval" (TOIS 2023) — arXiv 2210.11934
|
||||
- Elastic weighted RRF; Weaviate hybrid-search fusion algorithms; "Calibrated Fusion for Heterogeneous Graph-Vector Retrieval" — arXiv 2603.28886
|
||||
- bge-reranker — huggingface.co/BAAI/bge-reranker-base
|
||||
|
||||
**Concept-graph construction**
|
||||
- Zhang & Soh, "Extract-Define-Canonicalize" (EDC, EMNLP 2024) — arXiv 2404.03868; github.com/clear-nus/edc
|
||||
- KGGEN — arXiv 2502.09956; ReLiK (ACL 2024) — arXiv 2408.00103; LightKGG — arXiv 2510.23341; Google LangExtract — github.com/google/langextract
|
||||
|
||||
**Postgres vector storage**
|
||||
- pgvector — github.com/pgvector/pgvector; pgvectorscale — github.com/timescale/pgvectorscale; CNPG image-volume extensions — cloudnative-pg.io/docs/devel/imagevolume_extensions/; Supabase hybrid search — supabase.com/docs/guides/ai/hybrid-search
|
||||
- This cluster: `infra/docs/architecture/databases.md` (claude-memory-mcp is a CNPG tenant); this repo: `migrations/versions/001_initial_schema.py`, `src/claude_memory/api/app.py`
|
||||
|
||||
**Evaluation**
|
||||
- LoCoMo — arXiv 2402.17753; LongMemEval — arXiv 2410.10813; UMBRELA — arXiv 2406.06519; "Judging the Judges" / LLMJudge — arXiv 2502.13908; Voorhees & Buckley "Topic Set Size" (2002); Buckley & Voorhees "Bias and the Limits of Pooling"; BEIR — arXiv 2104.08663
|
||||
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