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Graph/aa2db651-fb3d-4f6c-a870-e602ca8c57f3

Domainunvalidated

Semantic Search

aa2db651-fb3d-4f6c-a870-e602ca8c57f3

Semantic Search

Node Details

Public graph metadata

Updated6/13/2026

PageRank0.0000

Effectiveness0.000

Connections

9 linked nodes

RELATES_TOComponent

RELATES_TOPackage

RELATES_TOComponent

RELATES_TOProblem

vector-dedup and semantic-search integration tests. Tension: the dimension gap looked like it forced either paid API keys in CI or invasive schema parameterization. Outcome: every embedding-dependent test skipped or failed.

PERTAIN_TOReport

Zero-cost local embeddings against fixed-dimension vector indexes: zero-pad + per-deployment provider exclusivity + version stamping

PERTAIN_TOProblem

vector-dedup and semantic-search integration tests. Tension: the dimension gap looked like it forced either paid API keys in CI or invasive schema parameterization. Outcome: every embedding-dependent test skipped or failed.

PERTAIN_TOSolution

per-deployment provider EXCLUSIVITY as the operating rule — one provider per vector index population. Tension: mixing spaces silently corrupts similarity. Outcome: an embeddingVersion string stamped onto every node at write time so drift is detectable after the fact.

PERTAIN_TOSolution

fail-LOUD when the local model can't load — the local model can't load. Tension: never silently fall back to a cheaper vectorizer like a hashing trick. Outcome: that swaps the geometry under the index.

PERTAIN_TOSolution

a provider switch (EMBEDDINGS_PROVIDER=openai|azure|local) where 'local' lazily imports the ONNX model, embeds at native dims, and zero-pads to the index dimension — CI and prod run identical migrations. Tension: schema untouched.

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