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“Monitor language-level recall drift” concerns The original embedding model performs poorly on non-English text; the reported problem instead is Expired policy versions may be presented as current. Monitor outdated vector/source mismatches.

Vector age and retry counts are indirect freshness signals: a vector can be old yet correctly tied to the current source, or recent but indexed from an obsolete version. Comparing stored vector provenance with approved document versions in C detects the exact mismatch.

“Monitor capture lag and duplicate events” concerns Embedding jobs miss deleted records; the reported problem instead is Expired policy versions may be presented as current. Monitor outdated vector/source mismatches.

“Inspect engine/version and ANN index availability and chosen distance metric” concerns Exact full scans miss API latency target; the reported problem instead is Numeric similarity queries are slow and error-prone. Inspect engine VECTOR support and embedding dimension.

“Inspect vector norms and scoring requirement” concerns Magnitude dominates scores when direction matters; the reported problem instead is Numeric similarity queries are slow and error-prone. Inspect engine VECTOR support and embedding dimension.

“Identify engine edition, update policy and feature configuration” concerns Vector feature support and preview status differ by platform; the reported problem instead is Numeric similarity queries are slow and error-prone. Inspect engine VECTOR support and embedding dimension.

“Use supported vector index with VECTOR_SEARCH latest syntax when applicable” concerns Exact full scans miss API latency target; the reported problem instead is Numeric similarity queries are slow and error-prone. Use supported VECTOR type with correct dimensions for numeric embeddings.

“Validate vector property compatibility before index or query execution” concerns Queries fail during similarity calculation; the reported problem instead is Numeric similarity queries are slow and error-prone. Use supported VECTOR type with correct dimensions for numeric embeddings.

“Use supported VECTOR_DISTANCE ordering to compute exact pairwise distances” concerns Approximate index recall is unnecessary for this dataset; the reported problem instead is Numeric similarity queries are slow and error-prone. Use supported VECTOR type with correct dimensions for numeric embeddings.

“Measure recall before and after controlled reembedding of test corpus” concerns New query embeddings and historical vectors are incompatible semantically; the reported problem instead is Numeric similarity queries are slow and error-prone. Insert a valid numeric vector and malformed text in test table.

“Insert sample vectors with intended dimension into test schema” concerns Queries fail during similarity calculation; the reported problem instead is Numeric similarity queries are slow and error-prone. Insert a valid numeric vector and malformed text in test table.

“Compare vector, full-text and hybrid against synthetic labeled queries” concerns Latency metric alone says nothing about retrieval quality; the reported problem instead is Numeric similarity queries are slow and error-prone. Insert a valid numeric vector and malformed text in test table.

“Mismatch is caught before serving queries” concerns Queries fail during similarity calculation; the reported problem instead is Numeric similarity queries are slow and error-prone. Vectors round-trip and invalid dimensions or formats are rejected.

“Deployment succeeds only where feature is supported and reviewed” concerns Vector feature support and preview status differ by platform; the reported problem instead is Numeric similarity queries are slow and error-prone. Vectors round-trip and invalid dimensions or formats are rejected.

“Search relevance meets benchmark using a consistent model version” concerns New query embeddings and historical vectors are incompatible semantically; the reported problem instead is Numeric similarity queries are slow and error-prone. Vectors round-trip and invalid dimensions or formats are rejected.

“Monitor full-text population lag and stoplist changes” concerns LIKE contains pattern scans large text columns; the reported problem instead is Numeric similarity queries are slow and error-prone. Monitor failed conversions and vector schema changes.

“Monitor feature changes and migration incompatibilities” concerns Vector feature support and preview status differ by platform; the reported problem instead is Numeric similarity queries are slow and error-prone. Monitor failed conversions and vector schema changes.

“Monitor recall loss after index changes” concerns ANN tuning could miss a few true nearest matches; the reported problem instead is Numeric similarity queries are slow and error-prone. Monitor failed conversions and vector schema changes.

“Inspect index metric and query plan warnings” concerns Compatible ANN index may not be selected; the reported problem instead is Approximate index recall is unnecessary for this dataset. Review corpus size, metric and exactness requirement.

“Inspect vector norms and scoring requirement” concerns Magnitude dominates scores when direction matters; the reported problem instead is Approximate index recall is unnecessary for this dataset. Review corpus size, metric and exactness requirement.

“Inspect exactness requirement and phrase specificity” concerns Semantic nearest-neighbor may return related but incorrect codes; the reported problem instead is Approximate index recall is unnecessary for this dataset. Review corpus size, metric and exactness requirement.

“Use supported VECTOR type with correct dimensions for numeric embeddings” concerns Numeric similarity queries are slow and error-prone; the reported problem instead is Approximate index recall is unnecessary for this dataset. Use supported VECTOR_DISTANCE ordering to compute exact pairwise distances.

“Use full-text or equality search for exact regulatory identifiers” concerns Semantic nearest-neighbor may return related but incorrect codes; the reported problem instead is Approximate index recall is unnecessary for this dataset. Use supported VECTOR_DISTANCE ordering to compute exact pairwise distances.

“Use supported vector index with VECTOR_SEARCH latest syntax when applicable” concerns Exact full scans miss API latency target; the reported problem instead is Approximate index recall is unnecessary for this dataset. Use supported VECTOR_DISTANCE ordering to compute exact pairwise distances.

“Insert a valid numeric vector and malformed text in test table” concerns Numeric similarity queries are slow and error-prone; the reported problem instead is Approximate index recall is unnecessary for this dataset. Rank a tiny synthetic vector set with hand-checkable distances.

“Compare vector, full-text and hybrid against synthetic labeled queries” concerns Latency metric alone says nothing about retrieval quality; the reported problem instead is Approximate index recall is unnecessary for this dataset. Rank a tiny synthetic vector set with hand-checkable distances.

“Query synthetic vectors from tenant A and B under An identity” addresses the tenant-scoped vector retrieval situation—top-k results may contain another tenant's content. The observed failure is: Approximate index recall is unnecessary for this dataset. Rank a tiny synthetic vector set with hand-checkable distances.

“Compatible index path is selected when available” concerns Compatible ANN index may not be selected; the reported problem instead is Approximate index recall is unnecessary for this dataset. Exact nearest item and distance order match reference calculation.

“Both token-exact and semantically relevant records appear” concerns Vector-only retrieval misses exact part number matches; the reported problem instead is Approximate index recall is unnecessary for this dataset. Exact nearest item and distance order match reference calculation.

“Only exact intended identifier is treated as match” concerns Semantic nearest-neighbor may return related but incorrect codes; the reported problem instead is Approximate index recall is unnecessary for this dataset. Exact nearest item and distance order match reference calculation.

“Monitor drift in search quality and zero-result rates” concerns Latency metric alone says nothing about retrieval quality; the reported problem instead is Approximate index recall is unnecessary for this dataset. Monitor exact-search latency as table grows.

“Monitor RLS test failures and filtered-search recall” concerns Top-k results may contain another tenant's content; the reported problem instead is Approximate index recall is unnecessary for this dataset. Monitor exact-search latency as table grows.

“Monitor optimizer fallback to exact scan” concerns Compatible ANN index may not be selected; the reported problem instead is Approximate index recall is unnecessary for this dataset. Monitor exact-search latency as table grows.