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lukaszraczylo
1d51d4cbbd
Framework runs a category/subcategory test catalog against multiple LLMs through an OpenAI-compatible endpoint. Deterministic evaluators score each response. Reports render as table, markdown, or JSON.
24 lines
697 B
Go
24 lines
697 B
Go
package tests
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import "math"
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// cosineSimilarity computes the cosine similarity of two equal-length
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// vectors: dot(a,b) / (|a| * |b|). Used to derive ground-truth expected
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// values for numeric tests at catalog-registration time rather than
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// hardcoding a pre-computed constant.
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func cosineSimilarity(a, b []float64) float64 {
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var dot, normA, normB float64
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for i := range a {
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dot += a[i] * b[i]
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normA += a[i] * a[i]
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normB += b[i] * b[i]
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}
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return dot / (math.Sqrt(normA) * math.Sqrt(normB))
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}
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// round4dp rounds v to 4 decimal places, matching the precision the
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// py-cosine prompt asks the model to answer to.
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func round4dp(v float64) float64 {
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return math.Round(v*10000) / 10000
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}
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