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Likith101
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Likith101
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CascadingRadium
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abhinavdangeti
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Feb 13, 2026
| options |= index.SkipFreqNorm | ||
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| if vectorIndexOptimizedFor == index.IndexOptimizedWithBivfFlat { | ||
| similarity = index.CosineSimilarity |
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Add commentary here, that this is for the backing index - as as default to hamming distance with Bivf.
| vectorIndexOptimizedFor = index.DefaultIndexOptimization | ||
| } | ||
| // BIVF-Flat index needs cosine similarity for correct scoring | ||
| // regardless of the similarity metric specified in the mapping. |
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Update to indicate that this is for the backing flat (ditto above)
| vectorIndexOptimizedFor = index.DefaultIndexOptimization | ||
| } | ||
| // BIVF-Flat index needs cosine similarity for correct scoring | ||
| // regardless of the similarity metric specified in the mapping. |
| effectiveOptimizedFor = index.DefaultIndexOptimization | ||
| } | ||
| if effectiveOptimizedFor == index.IndexOptimizedWithBivfFlat && field.Dims%8 != 0 { | ||
| return fmt.Errorf("field: '%s', vector dimensions for bivf-flat must be a multiple"+ |
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Update this to the same message - use a const for here and below. Maybe use - "incompatible vector dimensionality for BQ".
| // BIVF-Flat index optimization requires vector dimensions to be a multiple of 8 | ||
| if effectiveOptimizedFor == index.IndexOptimizedWithBivfFlat && field.Dims%8 != 0 { | ||
| return fmt.Errorf("field: '%s', invalid vector dimension: %d for optimization with BIVF-Flat,"+ | ||
| " dimension should be a multiple of 8", effectiveFieldName, field.Dims) |
| return nil, fmt.Errorf("k must be greater than 0 and vector must be non-empty") | ||
| } | ||
| if fieldMapping.VectorIndexOptimizedFor == index.IndexOptimizedWithBivfFlat { | ||
| similarityMetric = index.CosineSimilarity |
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Related to comment earlier.
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