geny-memory-adaptor 1.4.0

Synapse — a learnable, lightweight graph-traversal memory engine: BM25 + local static embeddings + typed-edge PageRank + an online-learned ranker, in one SQLite file with zero API calls.

geny-memory-adaptor

Synapse — a learnable, lightweight graph-traversal memory engine for AI agents.

Semantic search (local static embeddings) + keyword search (BM25) + graph traversal (typed-edge Personalized PageRank), fused by a tiny online-learned ranker that adapts to how your agent actually uses its memories. Everything lives in one SQLite file. numpy is the only dependency. Zero API calls, zero servers, zero idle cost — every operation is CPU milliseconds.

pip install geny-memory-adaptor

Why

Typical agent memory stacks pay an embedding API call per write AND per query, need a vector server, and never learn: every ranking weight is a hard-coded constant. Synapse inverts all three:

	typical stack	Synapse
query cost	1 embedding API call + vector store op	0 API calls, ~3–9 ms CPU (500–2,000 docs)
write cost	1 embedding API call + index rebuild	~0.3–0.7 ms, incremental SQLite upserts
infrastructure	vector DB / server	one .db file (+ a 32 MB fp16 table)
learning	none	online, per-event, microseconds

Quick start

from geny_memory_adaptor import SynapseMemory

mem = SynapseMemory.open("vault/synapse.db")          # or SynapseMemory.from_env()

# Write — idempotent by id. Links/tags feed the graph.
mem.index("note-1", "리듬게임에서 판정을 읽는 건 손이 먼저다",
          title="리듬게임 판정", tags=["게임"], links=["note-0"])

# Read — BM25 ∪ cosine seeds → per-type PageRank expansion → learned ranking.
hits = mem.search("리듬게임 판정", top_k=8)
for h in hits:
    print(h.id, round(h.score, 3), h.sources)   # e.g. ['bm25', 'vector', 'graph']

# Learn — tell it which shown memories were actually used.
mem.feedback(hits[0].query_token, used_ids=["note-1"])

# Optionally distill: if you already have better embeddings lying around
# (e.g. previously paid-for API vectors), hand them in as teachers…
mem.index("note-2", "text …", teacher_vec=my_stored_api_embedding)
mem.distill()   # bounded batch job; swaps the table ONLY if geometry improves

mem.close()

Configuration

Everything is a constructor argument, an environment variable (GMA_*), or a .env file — in that precedence order:

from geny_memory_adaptor import SynapseConfig, SynapseMemory
mem = SynapseMemory(SynapseConfig(path="synapse.db", dim=256, top_k=8))
# or
mem = SynapseMemory.from_env(dotenv=".env")   # GMA_PATH, GMA_DIM, GMA_TOP_K, …

How it works

write  ─ tokenize (words + char 2/3-grams; Korean-friendly, no morphology dep)
       ─ BM25 postings + local hash embedding (65,536 × 256 fp16 table)
       ─ typed edges: LINK (explicit) · TAG (IDF-weighted) · KNN (semantic)

query  ─ ① seeds: BM25 top-k ∪ cosine top-k, RRF-fused
       ─ ② expansion: one Personalized PageRank per edge type
            (LINK / TAG / KNN / CO-ACCESS) → 4 graph features
       ─ ③ ranking: 14 features → Linear(14→16) → GELU → Linear(16→1)

learn  ─ Hebbian: memories retrieved together AND both used strengthen a
         CO-ACCESS edge (lazy decay, no maintenance jobs)
       ─ ranker: pairwise logistic SGD on used-vs-ignored, with a safety
         blend gate — the learned score only participates once it BEATS the
         built-in heuristic on live online AUC (performance floor guarantee)
       ─ distill: fit the embedding table to caller-provided teacher vectors
         (closed feedback loop with a geometry-improvement gate)

Design notes:

• The heuristic floor. Scoring starts as a fixed hand-tuned linear combination. The MLP's influence (λ) stays 0 until it has ≥100 labelled events and a better pairwise win-rate — learning can help, never regress.
• Per-type PPR instead of a GNN. Each edge type gets its own PageRank feature; the ranker learns the type weights implicitly. No differentiable graph machinery, fully interpretable, milliseconds on thousands of nodes.
• Derived data only. The SQLite file is an index, not a store of record — delete it any time and re-index() from your source of truth.

geny-executor integration

geny_memory_adaptor.executor_adapter ships duck-typed handles matching geny-executor's memory Protocols (no import of geny-executor):

from geny_memory_adaptor import SynapseMemory, SynapseVectorHandle
handle = SynapseVectorHandle(SynapseMemory.open("vault/synapse.db"))
# → FileMemoryProvider(vector_store=handle)  # drop-in local vector layer

Korean retrieval (v1.0.0 — the headline feature)

Korean is a first-class citizen, engineered from NTCIR/ACL evidence and measured on a committed MIRACL-ko harness (213 human-judged queries / 2,835 wiki passages, Apache-2.0):

• No morphological analyzer needed: overlapping syllable bigrams (the NTCIR-validated recipe; trigrams measured −2.5 nDCG and are excluded), a guarded 조사/어미 stripper (받침-agreement gate, '의' protected, 하다-family only — +1.6 nDCG), cross-space bigrams for 붙여쓰기, and padded jamo 3/5-grams in the embedding stream only (+1.8 nDCG; keeping jamo out of BM25 avoids a measured 3× slowdown).
• Hybrid nDCG@10 0.593 · MRR@10 0.597 · R@10 0.911 on the harness — near analyzer-based BM25 (nori ≈ 0.64 on comparable data), pure Python.
• Robustness axes measured (Δ R@5 vs clean): 붙여쓰기 ±0.00 · 조사 변형 +0.02 · 자모 오타 −0.02 — the failure modes that break word-level Korean search simply don't apply.
• CI regression gate pins the quality floor (nDCG ≥ 0.55 on the fixture).

Run it yourself: python eval/run_eval.py (offline) · python eval/ablation.py.

Benchmarks (single CPU core, this repo's tests/bench.py)

index : 500 docs 0.32 ms/doc · 2,000 docs 0.71 ms/doc
search: ~3–16 ms/query at 500–2,800 docs (jamo embedding stream included)
learn : ambiguous-query demo — mean target rank 7.0 → 2.8 after 80 feedbacks
distill: 120 pairs, geometry corr 0.975 → 0.983, gate-approved swap, 8.5 s

License

Apache-2.0