vecdiff

Diff two embedding-index snapshots and get graded, evidence-first findings — for codebase vector-DB migrations (model swaps, re-chunking) and index rot audits (orphans, duplicates).

Fully local, deterministic, numpy-only. vecdiff never needs your original vector DB and never touches a network.

The standard advice for re-embedding is "run the new index side by side (blue/green), compare, then cut over." Nobody ships the compare step — teams throw a few queries at both indexes and go by feel. vecdiff mechanizes that comparison.

$ pip install vecdiff
$ vecdiff old-snapshot/ new-snapshot/ --gate

Why

A code-embedding index has three chronic anxieties:

1. Model swap — embedding spaces are not comparable vector-to-vector, so "did retrieval quality survive?" was unanswerable by machine.
2. Re-chunking / re-indexing — what exactly changed? had no answer at all.
3. Rot — code moves, indexes sit still. Chunks pointing at dead symbols and accidental duplicates accumulate quietly.

vecdiff answers all three with the same primitive: index-vs-index diff plus per-chunk health checks.

Quickstart

# 1. install (numpy is the only runtime dependency)
pip install vecdiff        # or: uv tool install vecdiff

# 2. generate two demo snapshots (deterministic; B simulates a sloppy re-embed)
python scripts/make_demo_snapshots.py /tmp/vecdemo

# 3. diff them
vecdiff /tmp/vecdemo/snapA /tmp/vecdemo/snapB --markdown report.md --gate
echo $?

You get console findings plus report.md, and --gate turns them into an exit code for CI (see below).

What it checks (v0.1)

Roadmap: N3 orphan/ghost chunks audited against a symbol graph (iOS IndexStoreDB / Kotlin), canonical-query supervised comparison, time-series rot monitoring.

Signal thresholds

vecdiff reports graded signals, never a verdict like "model B is better". Thresholds are stated inline in every finding:

The N2 outlier check is skipped (reported green, with the reason inline) when norm variance is ≈ 0 — e.g. embedders that return pre-normalized unit vectors, where z-scores would be float rounding noise. | N4 duplicate pairs (cosine ≥ threshold) / n | 0 | < 1% | ≥ 1% | | N5 largest bit-identical group | < 5 members | ≥ 5 members | ≥ 5% of index | | Q1 mean query Jaccard | ≥ 0.90 | ≥ 0.70 | < 0.70 | | Q1 heavy-loss queries (Jaccard ≤ 0.30) | < 2% | < 10% | ≥ 10% | | N3 orphan chunks / n | 0 | < 5% | ≥ 5% |

--gate exit codes: 0 all green, 1 any yellow, 2 any red. (Note: argparse usage errors — a mistyped flag — also exit 2; check stderr to tell them apart from a red verdict. Hard errors — unreadable snapshot, dimension mismatch — exit 3.)

Sampling

N1 is exact per queried chunk but samples shared ids by default (--sample 0.2, --seed 0 — same seed, same sample, reproducible reports). Use --full for an exact run; cost is O(queried × n × dim).

Case study — model swap over a real code index

371 chunks (~12k lines of real Python/Swift code), same chunks embedded with bge-small-en-v1.5 vs all-MiniLM-L6-v2:

• mean neighbor Jaccard 0.33; 42.6% of chunks lost ≥ 70% of their top-10 neighbors
• loss concentrated by directory (e.g. 32/50 chunks in one Swift core module, 22/59 in vecdiff itself) — the spot-check list for cutover
• N2/N4 confirmed both pipelines mechanically healthy (no scaling bug, no duplicates); gate exit 2 = do not cut over blind

Full story + reproducible commands: docs/case_study. Dogfooding this run also fixed a real tool bug (N2 now skips its norm-outlier check for pre-normalized embedders, with the reason inline).

Snapshot formats

A snapshot is a model-agnostic dump: chunk ids + float32 vectors + metadata (model, dim, chunk_paths, created_at). Loading validates the dump strictly and rejects duplicate ids, dimension/length mismatches, and non-finite (NaN/inf) vectors — a poisoned row would make cosine top-k silently arbitrary, so it fails at load instead of mid-report.

Format is auto-detected from the path; override with --format.

Any other vector DB (Qdrant, Chroma, LanceDB, pgvector, …): dump a JSONL snapshot with your DB's own client, or build one in memory with snapshot_from_arrays(ids=..., vectors=..., model=...) — vecdiff stays numpy-only and never talks to your database. Ready-to-run snippets live in docs/export_recipes.md. One rule above all: ids must be stable across the two snapshots (N1 pairs chunks by id), so never export row numbers.

CI migration gate

# .github/workflows/reindex-gate.yml — run before cutting traffic to the new index
- run: vecdiff snapshots/blue/ snapshots/green/ --full --gate
  # exit 2 (red) blocks the cutover step

vecdiff collects evidence for a human decision; the gate just makes "nobody looked" impossible.

Exit codes: 0 all green, 1 any yellow, 2 any red — and 3 for hard errors (bad snapshot, dimension mismatch, I/O failure), distinct from gate verdicts so CI can tell "the comparison failed" from "the comparison said no".

Method notes & honesty

• Cross-model comparison is impossible vector-to-vector — embedding spaces are unrelated. That is precisely why N1 compares neighbor-graph structure (per-chunk kNN Jaccard, Vectory-style) instead of coordinates. The method's prior art: Vectory (pentoai), which established kNN-IoU for embedding-space comparison in an ML-experiment-tracking frame; vecdiff operationalizes it for production code-index migrations and adds health checks Vectory does not have.
• N1 is a function of the candidate pool: neighbor identity depends on the whole index, so removing chunks changes even "clean" chunks' neighbor sets. Heavy-loss concentration by directory (chunk_paths) is where you should look first.
• N2 is an early-warning system, not a quality metric: a shifted norm distribution usually means pipeline scaling changed, not that retrieval got worse.
• Judgment stays human. vecdiff's job is findings like "14 chunks lost ≥70% of their top-10 neighbors, concentrated in src/auth (11)" — not scores.

Differentiation

Privacy

Everything runs locally: no network calls, no telemetry, snapshots stay on your machine. Reports contain chunk ids, paths, and similarity numbers — check them into the repo only if you're comfortable with that.

License

Apache-2.0