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“Monitor optimizer fallback to exact scan” concerns Compatible ANN index may not be selected; the reported problem instead is Semantic nearest-neighbor may return related but incorrect codes. Monitor false matches from semantic fallback.

“Monitor RLS test failures and filtered-search recall” concerns Top-k results may contain another tenant's content; the reported problem instead is Semantic nearest-neighbor may return related but incorrect codes. Monitor false matches from semantic fallback.

“Monitor model dimension drift” concerns Queries fail during similarity calculation; the reported problem instead is Semantic nearest-neighbor may return related but incorrect codes. Monitor false matches from semantic fallback.

“Inspect language, stopword and search requirements” concerns LIKE contains pattern scans large text columns; the reported problem instead is Top-k results may contain another tenant's content. Inspect RLS and filter order for vector search on target platform.

“Inspect vector norms and scoring requirement” concerns Magnitude dominates scores when direction matters; the reported problem instead is Top-k results may contain another tenant's content. Inspect RLS and filter order for vector search on target platform.

“Inspect model version recorded for each chunk and target similarity” concerns New query embeddings and historical vectors are incompatible semantically; the reported problem instead is Top-k results may contain another tenant's content. Inspect RLS and filter order for vector search on target platform.

“Measure recall@k, precision@k and ranking quality on a held-out test set” concerns Latency metric alone says nothing about retrieval quality; the reported problem instead is Top-k results may contain another tenant's content. Enforce tenant authorization before exposing retrieved rows.

“Align vector index metric to the query scoring requirement” concerns Compatible ANN index may not be selected; the reported problem instead is Top-k results may contain another tenant's content. Enforce tenant authorization before exposing retrieved rows.

“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 Top-k results may contain another tenant's content. Enforce tenant authorization before exposing retrieved rows.

“Insert sample vectors with intended dimension into test schema” concerns Queries fail during similarity calculation; the reported problem instead is Top-k results may contain another tenant's content. Query synthetic vectors from tenant A and B under A identity.

“Normalize synthetic equal-direction unequal-length vectors” concerns Magnitude dominates scores when direction matters; the reported problem instead is Top-k results may contain another tenant's content. Query synthetic vectors from tenant A and B under A identity.

“Query synthetic corpus with exact error IDs plus paraphrases” concerns Vector-only retrieval misses exact part number matches; the reported problem instead is Top-k results may contain another tenant's content. Query synthetic vectors from tenant A and B under A identity.

“Compatible index path is selected when available” concerns Compatible ANN index may not be selected; the reported problem instead is Top-k results may contain another tenant's content. No cross-tenant vector text or metadata appears.

“Only exact intended identifier is treated as match” concerns Semantic nearest-neighbor may return related but incorrect codes; the reported problem instead is Top-k results may contain another tenant's content. No cross-tenant vector text or metadata appears.

“Chosen retrieval meets pre-agreed relevance and latency thresholds” concerns Latency metric alone says nothing about retrieval quality; the reported problem instead is Top-k results may contain another tenant's content. No cross-tenant vector text or metadata appears.

“Monitor zero vectors and normalization failures” concerns Magnitude dominates scores when direction matters; the reported problem instead is Top-k results may contain another tenant's content. Monitor RLS test failures and filtered-search recall.

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

“Monitor recall loss after index changes” concerns ANN tuning could miss a few true nearest matches; the reported problem instead is Top-k results may contain another tenant's content. Monitor RLS test failures and filtered-search recall.

“Inspect model recommended similarity metric and normalization” concerns Euclidean distance changes ranking for varying-length vectors; the reported problem instead is New query embeddings and historical vectors are incompatible semantically. Inspect model version recorded for each chunk and target similarity.

“Inspect stored VECTORPROPERTY dimensions and model metadata” concerns Queries fail during similarity calculation; the reported problem instead is New query embeddings and historical vectors are incompatible semantically. Inspect model version recorded for each chunk and target similarity.

“Inspect RLS and filter order for vector search on target platform” concerns Top-k results may contain another tenant's content; the reported problem instead is New query embeddings and historical vectors are incompatible semantically. Inspect model version recorded for each chunk and target similarity.

“Align vector index metric to the query scoring requirement” concerns Compatible ANN index may not be selected; the reported problem instead is New query embeddings and historical vectors are incompatible semantically. Regenerate index vectors and rebuild/revalidate compatible index.

“Gate index usage on documented target-platform capability” concerns Vector feature support and preview status differ by platform; the reported problem instead is New query embeddings and historical vectors are incompatible semantically. Regenerate index vectors and rebuild/revalidate compatible index.

“Use matching cosine metric consistently in index and query” concerns Euclidean distance changes ranking for varying-length vectors; the reported problem instead is New query embeddings and historical vectors are incompatible semantically. Regenerate index vectors and rebuild/revalidate compatible index.

“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 New query embeddings and historical vectors are incompatible semantically. Measure recall before and after controlled reembedding of test corpus.

“Query synthetic corpus with exact error IDs plus paraphrases” concerns Vector-only retrieval misses exact part number matches; the reported problem instead is New query embeddings and historical vectors are incompatible semantically. Measure recall before and after controlled reembedding of test corpus.

“Benchmark both against a synthetic ground truth corpus” concerns ANN tuning could miss a few true nearest matches; the reported problem instead is New query embeddings and historical vectors are incompatible semantically. Measure recall before and after controlled reembedding of test corpus.

“The method meets required recall and performance targets” addresses the ann versus exact tradeoff situation—aNN tuning could miss a few true nearest matches. The observed failure is: New query embeddings and historical vectors are incompatible semantically. Search relevance meets benchmark using a consistent model version.

“Chosen retrieval meets pre-agreed relevance and latency thresholds” concerns Latency metric alone says nothing about retrieval quality; the reported problem instead is New query embeddings and historical vectors are incompatible semantically. Search relevance meets benchmark using a consistent model version.

“The query meets latency and recall thresholds against benchmark” addresses the vector_search approximate index situation—exact full scans miss API latency target. The observed failure is: New query embeddings and historical vectors are incompatible semantically. Search relevance meets benchmark using a consistent model version.

“Monitor failed conversions and vector schema changes” concerns Numeric similarity queries are slow and error-prone; the reported problem instead is New query embeddings and historical vectors are incompatible semantically. Monitor mixed-version vectors and staleness.

“Monitor metric mismatches between index and requests” concerns Euclidean distance changes ranking for varying-length vectors; the reported problem instead is New query embeddings and historical vectors are incompatible semantically. Monitor mixed-version vectors and staleness.

“Monitor zero vectors and normalization failures” concerns Magnitude dominates scores when direction matters; the reported problem instead is New query embeddings and historical vectors are incompatible semantically. Monitor mixed-version vectors and staleness.