Add a VectorDBBench client for Infino (vector search)

[muralikpbhat]

What

Adds a VectorDBBench client for Infino, an object-storage-native retrieval engine with a Python binding (the infino package). This PR covers vector search; FTS is a planned follow-on.

Client

• InfinoConfig / InfinoIndexConfig — connection tuning (catalog path, disk-cache budget/dir, object-store options) and the vector case config (cosine / L2 / inner-product metric map).
• search_mode (default hnsw_ivf) — the serving path, bridged to the engine's vector.search_mode config key rather than the binding API (so nothing goes vestigial if an engine default changes). hnsw_ivf serves a resident HNSW graph over the quantized vectors (with automatic IVF fallback); ivf serves the reclaimable IVF scan.
• ef (default 0) — a serve-time HNSW beam, bridged to vector.hnsw_ef_search. 0 serves each query at the graph's stamped k→ef curve; a positive value fixes the beam. See "Tracing the recall/QPS curve" below.
• Batched inserts — insert_embeddings buffers fed rows and commits them as one large append at a threshold (flushing any remainder when the load scope exits), so a load commits a handful of files regardless of the harness batch size instead of one file per fed batch. Insert speed is independent of NUM_PER_BATCH; the served index is unchanged.
• Runner — uses the stock MultiProcessingSearchRunner. The engine memory-maps its graph and shares one physical copy across reader processes via the page cache, so process-per-worker doesn't multiply the graph in RAM (no custom runner needed).
• _id → dataset-id map — built once per table, persisted beside the catalog, reloaded by each worker; invalidated on drop_old so a re-ingest remaps correctly.
• Cosine cases are normalized (need_normalize_cosine). Vector-only / NonFilter.

Tracing the recall/QPS curve

Because ef is a serve-time beam, the recall/QPS curve is traced build once, sweep at serve — the same one-ingest / serve-many shape as Zilliz's level or an HNSW engine's ef_search, so no re-ingest per point:

1. Ingest + build once (--drop-old).
2. Re-run search-only per beam (--skip-drop-old --skip-load), varying --ef over e.g. 128 192 256 384 512 768 1024 1536 2048 — a spread of operating points, densest near the top.

A plain run with no --ef (ef=0) serves at the graph's stamped k→ef curve — a single high-recall operating point (recall@100 ≈ 0.997 on Cohere-1M) — so infino produces a strong default point even without a sweep.

Registration

DB.Infino enum + init_cls / config_cls / case_config_cls; the Infino CLI command; a leaderboard color; and an infino optional-dependency extra (infino>=0.5.6).

Testing

• Unit tests: search_mode / ef validation, the config.yaml bridging, and insert-buffering (every fed row persisted across both the threshold flush and the final flush of a sub-threshold remainder).
• End-to-end: insert_embeddings → search_embedding round-trip (nearest-neighbor identity), plus a full Cohere-1M (768-dim) build-once ef sweep on hnsw_ivf tracing the recall/QPS curve.

Notes

• On-disk nothing changes upstream; the client only adds files and small registration hooks (no changes to shared runners).
• search_mode/ef are bridged through the engine config file because connect() currently exposes no equivalent keyword; the client writes a per-run config and points XDG_CONFIG_HOME at it before connecting.
• A follow-up will expose search_mode/ef as run-test UI inputs (frontend/config/dbCaseConfigs.py); this PR keeps the surface to the client and its registration.

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