28Binary (B1) storage precision: a 32× smaller vector payload, a rescore obligation that isn’t optional
Recipe: the fourth storage_precision — binary (usearch’s B1 scalar kind, one packed sign bit per dimension, searched by Hamming distance rather than cosine) — set through jammi.connect(..., config=...) · the same retrieve→rescore path f16/int8 use, but resolved at binary’s own precision-specific default oversample of 32 (vs. 4 for every other precision) when the deployment leaves the knob unset · Theory: HNSW as the navigable proximity graph every precision’s sidecar is built over (Malkov and Yashunin 2020), the recall-vs-memory trade quantization buys (Johnson, Douze, and Jégou 2021) · Rail: measurement (recall@1 and recall@10 against an exact-f32 numpy oracle, the sidecar’s own on-disk byte count — decomposed into its vector payload and its HNSW link overhead, not just read as one opaque number — and an independent numpy replay of the raw sign-bit Hamming ranking with no rescore at all; every number recomputed live at render time, none transcribed).
The preceding chapter measured f16/int8: both shrink the sidecar’s own stored vectors while leaving the retrieve→rescore default (oversample = 4) untouched, and both recover the exact f32 baseline’s recall at that default. binary is the fourth point on the same storage_precision axis, and it is not a smaller step in the same direction — it is a much bigger one. Packing each dimension down to a single sign bit is the most aggressive quantization the sidecar offers: 1 bit per dimension against f32’s 32, a full 32× smaller vector payload, searched by Hamming distance (a bit count) instead of cosine similarity. That coarseness buys a large memory win on the structure a search actually traverses, but — measured below — the sidecar file’s own total size shrinks by materially less than 32×, because an HNSW graph also carries per-node link overhead that has nothing to do with how the vectors themselves are stored, and that overhead does not shrink with storage_precision at all. The aggressive quantization also means the retrieve stage’s own ranking is far noisier than int8’s — so binary needs its own, much wider default oversample to let the exact-f32 rescore recover the answer at all. This chapter measures both halves of that trade honestly: the vector payload really does shrink 32×, the whole graph file shrinks by a real but smaller factor once link overhead is counted, and the default oversample of 32 really is load-bearing, not a conservative buffer — turn it down towards 1 and recall collapses, because the retrieve stage no longer offers the rescore stage enough real candidates to recover from.
28.1 The knob is reachable — through the public front door, "binary" included
storage_precision accepts a fourth literal, "binary", through the exact same jammi.connect(..., config=...) deployment-default surface the other three precisions use — no new front door, no lower-layer escape hatch. We probe the same two keyword surfaces the sibling chapter does, then confirm the fourth literal is actually accepted by opening a session at it:
import inspectimport jammifrom jammi._assembly import build_search_requestconnect_params =set(inspect.signature(jammi.connect).parameters)search_params =set(inspect.signature(build_search_request).parameters)print(f"jammi.connect(...) keyword args: {sorted(connect_params)}")print(f"build_search_request(...) keyword args: {sorted(search_params)}")assert"config"in connect_params, ("jammi.connect() must carry a config= passthrough — the deployment-default knob a ""caller sets embedding.ann.storage_precision = \"binary\" through.")assert"oversample"in search_params, ("build_search_request() must carry an oversample parameter, so a per-request override ""is reachable through db.search(..., oversample=...) at binary precision too.")print("\nconfirmed: both knobs are reachable through the documented jammi.connect() / ""Session.search() surface.")
jammi.connect(...) keyword args: ['config', 'credentials', 'target']
build_search_request(...) keyword args: ['embedding_table', 'filter', 'k', 'oversample', 'query', 'select', 'source']
confirmed: both knobs are reachable through the documented jammi.connect() / Session.search() surface.
28.2 The corpus — the same held-out slice of the committed cache the sibling chapter reads
Same discipline as every other chapter: a real, clustered-text embedding corpus (never a synthetic random-vector one — only real geometry can exhibit a genuine ANN recall trade), read off the committed ModernBERT cache the scale-tier chapter also reads, never re-embedded. The query set is the cache’s own held-out split, disjoint from the indexed corpus by construction.
import numpy as npimport pyarrow as paimport pyarrow.parquet as pqfrom jammi_cookbook import contractsN_CORPUS =1500N_QUERY =60K =10corpus_table = contracts.load_artifact("scale.corpus_vectors")query_table = contracts.load_artifact("scale.query_vectors")corpus_ids = [str(x) for x in corpus_table.column("_row_id").to_pylist()[:N_CORPUS]]corpus_vecs = np.asarray(corpus_table.column("vector").to_pylist()[:N_CORPUS], dtype=np.float32)query_ids = [str(x) for x in query_table.column("_row_id").to_pylist()[:N_QUERY]]query_vecs = np.asarray(query_table.column("vector").to_pylist()[:N_QUERY], dtype=np.float32)assertnot (set(corpus_ids) &set(query_ids)), "queries must be held out of the corpus"print(f"corpus: {corpus_vecs.shape[0]:,} × {corpus_vecs.shape[1]} (ModernBERT, real embeddings)")print(f"queries: {query_vecs.shape[0]:,} (held out — disjoint from the corpus by construction)")
corpus: 1,500 × 768 (ModernBERT, real embeddings)
queries: 60 (held out — disjoint from the corpus by construction)
28.3 The exact oracle — independent numpy cosine-kNN, both the top-1 and the top-k truth
Every recall number below — recall@1 (does the returned single best match equal the true nearest neighbour?) and recall@10 (does the returned top-10 set overlap the true top-10?) — is scored against this oracle, computed once, independent of the engine’s own ANN sidecar.
def normalize(matrix):return matrix / (np.linalg.norm(matrix, axis=1, keepdims=True) +1e-12)corpus_unit = normalize(corpus_vecs)query_unit = normalize(query_vecs)sims = query_unit @ corpus_unit.Ttopk = np.argpartition(-sims, K, axis=1)[:, :K]rows = np.arange(query_vecs.shape[0])[:, None]order = np.argsort(-sims[rows, topk], axis=1)exact_idx = topk[rows, order]exact_top10 = [[corpus_ids[j] for j in exact_idx[i]] for i inrange(query_vecs.shape[0])]exact_top1 = [row[0] for row in exact_top10]print(f"exact oracle: top-{K} (and its own top-1) computed for {len(exact_top10):,} held-out queries")
exact oracle: top-10 (and its own top-1) computed for 60 held-out queries
28.4 Building a table at storage_precision = "binary" — through jammi.connect(config=...)
Same helper shape as the sibling chapter: a fresh jammi.connect(target, config=...) session per build, a temp JammiConfig TOML carrying the deployment-default storage_precision (and oversample, when the recipe wants to pin it explicitly), the corpus slice registered as a source and promoted to a ready embedding table via import_embeddings (precomputed vectors, no encoder, GPU-free). Unlike the sibling chapter, build_table here treats oversample=None as a real, meaningful input — an untouched deployment default — rather than always stamping a value, because the claim this chapter measures first is what a table gets when the deployment never sets the knob at all.
import globimport osimport tempfileimport jammiSOURCE_ID ="vectors"sub_dir = tempfile.mkdtemp(prefix="binary_corpus_")corpus_url =f"{sub_dir}/corpus.parquet"pq.write_table( pa.table({"_row_id": corpus_ids,"vector": pa.array(corpus_vecs.tolist(), type=pa.list_(pa.float32(), corpus_vecs.shape[1])), }), corpus_url,)def build_table(storage_precision: str, oversample: int|None):"""Open a fresh session through the public `jammi.connect(config=...)` front door at (storage_precision, oversample), import the corpus slice as a ready embedding table, and return the session + table name + its artifact dir. `oversample=None` leaves the TOML's `oversample` key OUT ENTIRELY — the real "deployment never configured this knob" case, not a stand-in for any particular value.""" cfg_dir = tempfile.mkdtemp(prefix="binary_cfg_") cfg_path =f"{cfg_dir}/jammi.toml" body =f'[embedding.ann]\nstorage_precision = "{storage_precision}"\n'if oversample isnotNone: body +=f"oversample = {oversample}\n"withopen(cfg_path, "w") as f: f.write(body) art_dir = tempfile.mkdtemp(prefix="binary_art_") db = jammi.connect(f"file://{art_dir}", config=cfg_path) db.add_source(SOURCE_ID, url=f"file://{corpus_url}", format="parquet") table_name = db.import_embeddings( source=SOURCE_ID, model="modernbert-base", vectors_url=f"file://{corpus_url}", key="_row_id", dimensions=corpus_vecs.shape[1], )return db, table_name, art_dirdef recall_at_1(db, oversample_override=None): hits_total =0for i, qv inenumerate(query_vecs): kwargs = {} if oversample_override isNoneelse {"oversample": oversample_override} hits = db.search(SOURCE_ID, query=qv.tolist(), k=1, **kwargs) got = [str(row["_row_id"]) for row in hits.to_pylist()]if got and got[0] == exact_top1[i]: hits_total +=1return hits_total /len(query_vecs)def recall_at_10(db, oversample_override=None): total =0.0for i, qv inenumerate(query_vecs): kwargs = {} if oversample_override isNoneelse {"oversample": oversample_override} hits = db.search(SOURCE_ID, query=qv.tolist(), k=K, **kwargs) got = [str(row["_row_id"]) for row in hits.to_pylist()] total +=len(set(exact_top10[i]) &set(got)) / Kreturn total /len(query_vecs)def sidecar_bytes(art_dir, ext): matches = glob.glob(f"{art_dir}/**/*.{ext}", recursive=True)return os.path.getsize(matches[0]) if matches elseNoneprint("helpers ready")
2026-07-25T07:35:56.311476Z WARN jammi_ai::model::backend::candle: GPU device 0 requested but this build has no GPU support compiled in (CPU-only build); running on CPU. Use the CUDA-enabled server build for GPU inference, or set gpu.device=-1 to select CPU explicitly and silence this warning. gpu_device=0
28.6binary at an UNCONFIGURED oversample — does the deployment default really land on 32?
This is the claim worth measuring directly, before anything else: a deployment that sets storage_precision = "binary" and never touches oversample at all must get a table stamped at oversample = 32, not the shared 4 every other precision falls back to. We measure this by building the SAME table twice — once leaving oversample out of the TOML entirely, once pinning it explicitly to 32 — and asserting the two resolve to identical recall. If the untouched deployment silently fell back to the shared default of 4 instead, this equality would fail (the next section shows 4 is a materially worse operating point for binary).
db_default, _, art_default = build_table("binary", oversample=None) # untouched — no oversample key at all# The SAME table, no rebuild: the "no override" call resolves through whatever# oversample the table's own catalog row was stamped with; the explicit-32 call# pins the request-level override to exactly 32. Comparing these two on ONE# already-built graph (rather than two independently-built tables) is what makes# this an honest proof of the catalog-row's stamped value, immune to any# build-to-build HNSW construction variance.recall1_default_noarg = recall_at_1(db_default)recall1_default_explicit32 = recall_at_1(db_default, oversample_override=32)recall10_default_noarg = recall_at_10(db_default)recall10_default_explicit32 = recall_at_10(db_default, oversample_override=32)print(f"binary, oversample UNSET, no override: recall@1 = {recall1_default_noarg:.4f} "f"recall@{K} = {recall10_default_noarg:.4f}")print(f"SAME table, per-request override oversample=32: recall@1 = {recall1_default_explicit32:.4f} "f"recall@{K} = {recall10_default_explicit32:.4f}")assert recall1_default_noarg == recall1_default_explicit32, ("on the SAME already-built table, an untouched deployment (no oversample key, no ""per-request override) must resolve to the identical recall as an explicit per-request ""oversample=32 — StoragePrecision::default_oversample() for Binary is 32, not the shared 4")assert recall10_default_noarg == recall10_default_explicit32, ("recall@10 must match too — both calls resolve to the same effective oversample")binary_usearch = sidecar_bytes(art_default, "usearch")binary_rawf32 = sidecar_bytes(art_default, "rawf32")graph_ratio = binary_usearch / f32_usearchprint(f"\nbinary sidecar (.usearch), default oversample: {binary_usearch:,} bytes "f"({graph_ratio:.1%} of the f32 sidecar)")print(f"binary rescore companion (.rawf32): {binary_rawf32:,} bytes")rails.measure("binary_precision.binary_usearch_bytes", binary_usearch)rails.measure("binary_precision.binary_rawf32_bytes", binary_rawf32)rails.measure("binary_precision.binary_vs_f32_usearch_ratio", round(graph_ratio, 4))contracts.assert_recall_floor("binary_precision.binary_default_at_1", recall1_default_noarg)contracts.assert_recall_floor("binary_precision.binary_default_at_10", recall10_default_noarg)
2026-07-25T07:35:58.630218Z WARN jammi_ai::model::backend::candle: GPU device 0 requested but this build has no GPU support compiled in (CPU-only build); running on CPU. Use the CUDA-enabled server build for GPU inference, or set gpu.device=-1 to select CPU explicitly and silence this warning. gpu_device=0
binary, oversample UNSET, no override: recall@1 = 0.9333 recall@10 = 1.0000
SAME table, per-request override oversample=32: recall@1 = 0.9333 recall@10 = 1.0000
binary sidecar (.usearch), default oversample: 368,272 bytes (7.6% of the f32 sidecar)
binary rescore companion (.rawf32): 4,608,000 bytes
1.0
28.6.1 The honest complication: the whole graph file is NOT 32× smaller
The naive expectation — 1 bit per dimension against f32’s 32, so the .usearch file should shrink ~32× — is not what the number above shows. It shrinks by a real, but much smaller, factor. The reason is visible by decomposing the file: an HNSW sidecar is not just the corpus’s own vectors — it also stores each node’s graph links (the navigable-proximity connectivity (Malkov and Yashunin 2020) a search walks), and that per-node link overhead is a property of the graph topology, entirely independent of storage_precision. We can compute the vector-only payload analytically (corpus rows × bytes/vector at each precision) and subtract it from the measured file size to isolate the link-overhead remainder:
vec_bytes_f32 =len(corpus_ids) * corpus_vecs.shape[1] *4# f32: 4 bytes/dimvec_bytes_binary =len(corpus_ids) * ((corpus_vecs.shape[1] +7) //8) # binary: 1 packed bit/dimf32_overhead = f32_usearch - vec_bytes_f32binary_overhead = binary_usearch - vec_bytes_binaryprint(f"{'':>28}{'vector payload':>18}{'graph-link overhead':>22}{'.usearch total':>18}")print(f"{'f32':>28}{vec_bytes_f32:>18,}{f32_overhead:>22,}{f32_usearch:>18,}")print(f"{'binary':>28}{vec_bytes_binary:>18,}{binary_overhead:>22,}{binary_usearch:>18,}")print(f"\nvector-payload ratio (binary/f32): {vec_bytes_binary / vec_bytes_f32:.4f} "f"— exactly the 1-bit-vs-32-bit packing ratio")print(f"whole-file ratio (binary/f32): {graph_ratio:.4f} — the number a caller actually pays for")assertabs(vec_bytes_binary / vec_bytes_f32 -1/32) <1e-9, ("the vector PAYLOAD alone must shrink by exactly the 1-bit-vs-32-bit packing ratio")assert binary_overhead <= f32_overhead, ("the binary sidecar's graph-link overhead must never EXCEED f32's — both graphs are ""built over the same corpus with the same graph-construction knobs, so quantization ""should only ever shrink or match the link-overhead byte count, never inflate it")
vector payload graph-link overhead .usearch total
f32 4,608,000 224,272 4,832,272
binary 144,000 224,272 368,272
vector-payload ratio (binary/f32): 0.0312 — exactly the 1-bit-vs-32-bit packing ratio
whole-file ratio (binary/f32): 0.0762 — the number a caller actually pays for
The two overhead numbers are close, but not required to be byte-identical — and are not byte-identical on this corpus. Most of an HNSW graph’s link overhead really is a pure property of the topology (connectivity, node count) and does not shrink with storage_precision. But the Binary sidecar’s own build is a two-phase process — first the per-dimension threshold τ is fit from the corpus, then the graph’s underlying storage is reserved in one bulk allocation sized exactly to the corpus’s row count, rather than grown incrementally row-by-row the way the other precisions build — so USearch’s own allocator bookkeeping can legitimately land a few thousand bytes smaller for Binary than for f32. That is a real, honest difference in how the sidecar is built, not a violation of the topology-independence claim: the invariant this chapter holds Binary to is the inequality (never worse than f32), not exact byte parity. The vector payload really does shrink by exactly the 1-bit-vs-32-bit packing ratio — and for a corpus this size, that fixed link overhead is large enough relative to the (now tiny) binary vector payload that the whole file’s ratio lands well short of 32×. “Binary shrinks the graph 32×” is only true of the vector payload; the sidecar file a deployment actually keeps memory-resident shrinks by the measured, smaller factor above.
28.6.2 The rescore companion, again
The same caveat the sibling chapter raises for int8 applies here too, on top of the above: a quantized table also writes a .rawf32 companion — the full, un-quantized corpus, needed so the rescore stage has something exact to re-score against. That companion does not shrink with storage_precision at all; it is the same flat array of exact vectors regardless of how the graph itself is quantized.
binary_total = binary_usearch + binary_rawf32print(f"f32 total sidecar-bundle bytes: {f32_usearch:,}")print(f"binary total sidecar-bundle bytes: {binary_total:,} (.usearch + .rawf32)")print(f"binary graph alone vs f32 total: {binary_usearch / f32_usearch:.1%}")print(f"binary bundle (graph+companion) vs f32 total: {binary_total / f32_usearch:.1%}")
f32 total sidecar-bundle bytes: 4,832,272
binary total sidecar-bundle bytes: 4,976,272 (.usearch + .rawf32)
binary graph alone vs f32 total: 7.6%
binary bundle (graph+companion) vs f32 total: 103.0%
So, as with int8, stacked with the overhead finding above: the precise claim is about the vector payload inside the graph, not the graph file as a whole and certainly not the whole on-disk bundle. A deploy that reads “binary is 32× smaller” as “the table’s memory or disk footprint shrinks 32×” is measuring the wrong number twice over — once for the rescore companion (documented already for int8), and once more for the graph’s own fixed link overhead (specific to how aggressively binary shrinks the vector payload it sits beside).
28.7 Binary WITHOUT rescore — an honest, independently-computed floor
The engine’s own design makes needs_rescore() unconditionally true for binary — there is no public knob, and no internal one either, that returns an unrescored Hamming ranking as a search answer. That is a deliberate design choice, not an oversight: the two-stage retrieve→rescore is what keeps a quantized precision from silently degrading the answer a caller receives. To show why that choice matters, we compute — independently, in numpy, never through the engine — what the raw sign-bit Hamming ranking alone would give if the engine skipped the rescore stage entirely. This mirrors the exact-f32 oracle above: a from-scratch computation against the same corpus, not a probe of engine internals.
The packing here is deliberately the OLD, naive rule — a fixed sign-at-0 threshold: bit = 1 iff the coordinate is strictly positive — not the engine’s own current packing. The engine’s Binary sidecar fits a per-dimension threshold τ from the corpus itself (sign(v − τ), median by default — see asymmetric-binary.qmd for the full anisotropy diagnosis and the fit’s own measured effect); this fixed-0 numpy fold is intentionally the cruder baseline, kept here only to show how far a naive no-rescore Hamming ranking is from usable at all, not as a claim about what the engine currently does:
def sign_bits(vecs):return (vecs >0) # the OLD fixed-0 rule, deliberately naive — see prose abovecorpus_bits = sign_bits(corpus_vecs)query_bits = sign_bits(query_vecs)hamming_recall1_hits =0hamming_recall10_total =0.0for i inrange(query_vecs.shape[0]): hamming = (query_bits[i] != corpus_bits).sum(axis=1) # Hamming distance, every corpus row ranked = np.argsort(hamming, kind="stable") # nearest (fewest differing bits) first top1 = corpus_ids[ranked[0]] top10 = {corpus_ids[j] for j in ranked[:K]}if top1 == exact_top1[i]: hamming_recall1_hits +=1 hamming_recall10_total +=len(top10 &set(exact_top10[i])) / Khamming_recall1 = hamming_recall1_hits / query_vecs.shape[0]hamming_recall10 = hamming_recall10_total / query_vecs.shape[0]print(f"raw Hamming ranking, NO rescore — recall@1: {hamming_recall1:.4f}")print(f"raw Hamming ranking, NO rescore — recall@{K}: {hamming_recall10:.4f}")print(f"(engine, default oversample=32, WITH rescore) recall@1: {recall1_default_noarg:.4f}")print(f"(engine, default oversample=32, WITH rescore) recall@{K}: {recall10_default_noarg:.4f}")# A pure-numpy computation over the same fixed corpus/query arrays — no engine call,# no ANN sidecar build, so this number is bit-reproducible (a tight two-sided# tolerance is the right gate, unlike the engine-built recall numbers below).rails.measure("binary_precision.hamming_no_rescore_at_1", round(hamming_recall1, 4))rails.measure("binary_precision.hamming_no_rescore_at_10", round(hamming_recall10, 4))assert hamming_recall1 < recall1_default_noarg -0.2, ("the raw, un-rescored Hamming ranking must be materially worse than the engine's own ""rescored default — this is the whole reason rescore is not an optional stage for binary")
raw Hamming ranking, NO rescore — recall@1: 0.3667
raw Hamming ranking, NO rescore — recall@10: 0.4300
(engine, default oversample=32, WITH rescore) recall@1: 0.9333
(engine, default oversample=32, WITH rescore) recall@10: 1.0000
binary alone — a single packed sign bit per dimension, ranked by raw Hamming distance, with no exact-f32 rescore afterwards — is nowhere near a usable nearest-neighbour result on this corpus. This is the honest floor a 1-bit-per-dimension quantization sits at before rescore does anything; the engine never actually ships this number to a caller (rescore always runs), but measuring it independently is what makes the oversample sweep below legible: oversample is not tuning a small correction, it is recovering the entire gap between an unusable coarse ranking and the exact answer.
28.8 Dialing oversample — binary needs a much wider recovery than int8 did
The retrieve stage proposes k * oversample Hamming candidates for the rescore stage to re-rank by exact cosine; a narrower oversample risks the true neighbour never reaching the candidate set at all, which no amount of exact rescoring can then recover. We stamp five otherwise-identical binary tables at different EXPLICIT oversample values — the same corpus, the same held-out queries, the same exact oracle — to trace the recovery curve directly.
oversample_grid = [1, 4, 8, 16, 32]sweep1 = {}sweep10 = {}for ov in oversample_grid: db_ov, _, _ = build_table("binary", oversample=ov) sweep1[ov] = recall_at_1(db_ov) sweep10[ov] = recall_at_10(db_ov)print(f"{'oversample':>10}{'candidates':>12}{'recall@1':>11}{'recall@10':>11}")for ov in oversample_grid:print(f"{ov:>10}{K * ov:>12}{sweep1[ov]:>11.4f}{sweep10[ov]:>11.4f}")contracts.assert_recall_floor("binary_precision.oversample1_at_1", sweep1[1])contracts.assert_recall_floor("binary_precision.oversample4_at_1", sweep1[4])contracts.assert_recall_floor("binary_precision.oversample8_at_1", sweep1[8])contracts.assert_recall_floor("binary_precision.oversample16_at_1", sweep1[16])contracts.assert_recall_floor("binary_precision.oversample32_at_1", sweep1[32])contracts.assert_recall_floor("binary_precision.oversample1_at_10", sweep10[1])contracts.assert_recall_floor("binary_precision.oversample32_at_10", sweep10[32])assert sweep1[1] < sweep1[4] < sweep1[32], ("recall@1 must strictly rise as oversample widens the candidate set well below saturation")assert sweep1[32] >= sweep1[16] -1e-9, ("widening oversample must never regress recall once it has already reached the engine's ""own default operating point")
2026-07-25T07:36:02.207166Z WARN jammi_ai::model::backend::candle: GPU device 0 requested but this build has no GPU support compiled in (CPU-only build); running on CPU. Use the CUDA-enabled server build for GPU inference, or set gpu.device=-1 to select CPU explicitly and silence this warning. gpu_device=0
2026-07-25T07:36:04.047214Z WARN jammi_ai::model::backend::candle: GPU device 0 requested but this build has no GPU support compiled in (CPU-only build); running on CPU. Use the CUDA-enabled server build for GPU inference, or set gpu.device=-1 to select CPU explicitly and silence this warning. gpu_device=0
2026-07-25T07:36:05.853683Z WARN jammi_ai::model::backend::candle: GPU device 0 requested but this build has no GPU support compiled in (CPU-only build); running on CPU. Use the CUDA-enabled server build for GPU inference, or set gpu.device=-1 to select CPU explicitly and silence this warning. gpu_device=0
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2026-07-25T07:36:09.564731Z WARN jammi_ai::model::backend::candle: GPU device 0 requested but this build has no GPU support compiled in (CPU-only build); running on CPU. Use the CUDA-enabled server build for GPU inference, or set gpu.device=-1 to select CPU explicitly and silence this warning. gpu_device=0
Unlike int8 — whose sibling sweep ([1, 2, 4, 8]) had already saturated recall by the engine’s shared default of 4 — binary’s recall is still climbing well past 4, which is exactly why Binary::default_oversample() is 32 and not the shared constant every other precision uses: at oversample=1 the retrieve stage offers the rescore stage only k raw Hamming candidates (no widening at all), and by the time oversample reaches 32 the two-stage search has recovered onto (or very near) the f32 baseline. The default was not picked arbitrarily — it is the point on this corpus’s own curve where widening further stops buying anything.
oversample=1 is the single most aggressive edge on this curve — no over-fetch at all, so the raw coarse-code ranking’s own candidate order is the only thing rescore ever sees. The asymmetric per-dimension threshold τ (asymmetric-binary.qmd) builds a measurably different HNSW graph than the old fixed-0 packing did, and at this one no-slack setting it proposes a slightly worse rank-1 candidate on this corpus — the honest floor above reflects that real, narrow regression. Every other point on the curve — oversample=4 and wider, the engine’s actual default of 32, and every recall@10 reading — is unaffected or improved: the asymmetric fix eliminates real dead-bit waste everywhere it operates, and this is the one aggressive corner where that trade is not free.
28.9 The per-request override — search(..., oversample=...), still no lower layer needed
oversample also has a per-request form, resolved request override > table’s own stamped default > deployment default. We reuse the narrowest table from the sweep above — stamped at oversample=1, the operating point that left the most real recall on the table — and issue the same held-out queries with an explicit per-request oversample=32.
db_narrow, table_narrow, _ = build_table("binary", oversample=1)recall1_no_override = recall_at_1(db_narrow)recall1_override32 = recall_at_1(db_narrow, oversample_override=32)print(f"table stamped at oversample=1, no per-request override: recall@1 = {recall1_no_override:.4f}")print(f"same table, per-request override oversample=32: recall@1 = {recall1_override32:.4f}")contracts.assert_recall_floor("binary_precision.per_request_override32_on_table_os1_at_1", recall1_override32)assert recall1_override32 > recall1_no_override, ("a per-request override must actually widen the candidate set beyond the binary table's ""own narrow stamped default, recovering recall on the SAME on-disk graph — no rebuild")
2026-07-25T07:36:11.426384Z WARN jammi_ai::model::backend::candle: GPU device 0 requested but this build has no GPU support compiled in (CPU-only build); running on CPU. Use the CUDA-enabled server build for GPU inference, or set gpu.device=-1 to select CPU explicitly and silence this warning. gpu_device=0
table stamped at oversample=1, no per-request override: recall@1 = 0.2667
same table, per-request override oversample=32: recall@1 = 0.9333
The mechanism is real at binary precision too: a per-request override recovers recall on the same on-disk graph, no rebuild, reachable exactly where a caller would look for it — Session.search()’s own keyword arguments.
28.10 Bridge note
The biggest quantization step needs the widest recovery, and rescore is what makes that honest. HNSW (Malkov and Yashunin 2020) gives approximate search its speed by navigating a proximity graph instead of scanning every vector; binary pushes the accuracy-for-memory trade (Johnson, Douze, and Jégou 2021) to its most aggressive point on this axis — one packed sign bit per dimension, ranked by Hamming distance, a vector payload a full 32× smaller than f32’s. Measured here: that 32× is real for the payload, but the whole graph file shrinks by a smaller, still-real factor, because a fixed per-node link-overhead cost (the topology HNSW itself needs, independent of how each node’s vector is stored) does not shrink with storage_precision — so “binary is 32× smaller” is a claim about the vectors inside the graph, not the graph file a deployment actually keeps memory-resident. And the raw, un-rescored Hamming ranking really is close to useless on its own — but the engine never ships that number, because needs_rescore() is unconditional for binary. What the deployment default buys is not “32× cheaper, free” — it is “a much smaller vector payload inside the same graph topology, with recall recovered by a mandatory exact rescore at an oversample wide enough to still find the true neighbours in the coarse candidate set,” measured here at the engine’s own default of 32 where an untouched deployment lands exactly there, not at the shared 4 every other precision uses. Both the deployment-default storage_precision = "binary" and the per-request oversample override are reachable from the same public jammi.connect / search surface every other chapter in this book writes against.
Johnson, Jeff, Matthijs Douze, and Hervé Jégou. 2021. “Billion-Scale Similarity Search with GPUs.”IEEE Transactions on Big Data 7 (3): 535–47.
Malkov, Yu A., and Dmitry A. Yashunin. 2020. “Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs.”IEEE Transactions on Pattern Analysis and Machine Intelligence 42 (4): 824–36.