fix: rebuild local-mode vectors on reload the way the write path stores them - #1381
fix: rebuild local-mode vectors on reload the way the write path stores them#1381Gronoxx wants to merge 3 commits into
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The cosine normalization applied on write is spelled out inline in _add_point and _update_vectors, for dense vectors and multivectors alike. Extract normalize_dense/normalize_multivector so every write path shares one definition. No behavior change.
Cosine collections store unit-normalized float32 vectors: _add_point casts the incoming vector with np.array(vector, dtype=np.float32) and then normalizes it. load_vectors() did neither -- it handed the raw persisted lists straight to np.array() -- so a collection reopened from disk held un-normalized float64 vectors. Three consequences, all reproducible: * retrieve(with_vectors=True) returned a different vector for the same point depending on whether the collection had been reopened, and disagreed with the server, which returns the normalized vector; * cosine_similarity() normalizes its candidate set in place, so the first search over a reopened collection silently rewrote its vectors, and scores differed between a freshly written collection and the same collection reloaded; * float64 doubled the in-RAM footprint of every reopened collection. _update_point had the second half of the same problem: it normalized before casting, so overwriting a point stored slightly different values than inserting it fresh. It now follows _add_point and _update_vectors.
The suite already reopens a collection from disk (test_search_with_persistence), but only compares scored search results, which agree within rel_tol=1e-4 even when the stored vectors do not. compare_collections compares the vectors themselves, and that is what catches a reload that fails to rebuild them the way the write path stored them. Covers dense and multivector fixtures, which between them exercise cosine, dot and euclidean distances.
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📝 WalkthroughWalkthroughAdded shared dense and multivector cosine normalization helpers with zero-norm protection. Applied them to vector loading, insertion, replacement, point updates, and named-vector updates. Added persistence tests for dense and multivector vectors, repeated upserts, score stability, unit normalization, and zero-norm vectors. Estimated code review effort: 3 (Moderate) | ~20 minutes Merge Risk: 🔵 Low · up to The change fixes vector normalization and dtype consistency after reload, but near-zero multivector inputs still use a different normalization cutoff than dense vectors, which can cause bounded value or score differences. The PR is mergeable with explicit owner awareness or a follow-up to align this edge case. Suggested reviewers: 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
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✨ Finishing Touches🧪 Generate unit tests (beta)
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In `@qdrant_client/local/local_collection.py`:
- Around line 97-100: Update normalize_multivector to preserve token vectors
whose norms are at or below EPSILON, matching normalize_dense, instead of
dividing them by EPSILON; retain normalization only for norms above the cutoff.
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qdrant_client/local/local_collection.pytests/congruence_tests/test_persistence.pytests/test_local_persistence.py
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Thanks for running this yourself, and for pinning to 0.4.3 instead of treating a newer Ruff as evidence. That distinction matters here. The four E731s are mine, from the new tests in this PR. The repo's pre-commit runs ruff-format only, with --line-length=99, and the ruff linter hook is commented out right above it with # ToDo: re-introduce ruff linter later. Ruff doesn't show up in any CI workflow either. Under the hook that actually runs, all three files this PR touches come back already formatted. That said, they're the only E731s in the repo, so the lambdas do stand out. Happy to switch them to local def helpers if a maintainer wants that. I'd rather hold off for now than push a commit that resets CI over a rule the project currently has disabled. |
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devbranch. Did you create your branch fromdev? Yes.Changes to Core Features:
What & why
In local mode, a cosine collection is supposed to keep its vectors unit-normalized and stored as
float32._add_pointdoes both, casting withnp.array(vector, dtype=np.float32)and then normalizing.load_vectorsdoes neither, so once you reopen a collection from disk you get back the raw persisted lists asfloat64.That causes three problems:
retrievestops agreeing with the server. A real Qdrant 1.18.1 gives you the normalized vector, and so does a fresh local collection. A reopened one doesn't.cosine_similaritynormalizes its candidate set in place, so the first search over a reopened collection quietly rewrites the stored vectors.retrievegives you different values before and after that search.float64doubles the in-RAM footprint of every reopened collection._update_pointhad half of the same bug. It normalized before casting, which is the opposite order from_add_pointand_update_vectors, so overwriting a point stored slightly different values than inserting it, and updating a multivector promoted it tofloat64.test_search_with_persistencedoes reopen a collection and check it against the server, but it only compares scored results withinrel_tol=1e-4, and the score difference here lands around 1e-8.compare_collectionscompares the vectors themselves, which is what actually catches this.The change is split into three commits: first extract the normalization into
normalize_denseandnormalize_multivectorwith no behavior change, then fixload_vectorsand_update_point, then add the congruence test.How is this tested?
tests/congruence_tests/test_persistence.pythat compare a reopened collection against the server, across dense and multivector fixtures. Between them they cover cosine, dot and euclidean.devand pass with the change. The sixth pins the zero-norm case, which behaves the same either way but isn't covered anywhere in the suite today.devwhen both runs use the same seed. The fixtures are randomized, so raw failure counts move around between runs.