mirror of
https://github.com/saymrwulf/KnowledgeRefinery.git
synced 2026-05-14 20:47:51 +00:00
38a99476d6
macOS app for corpus ingestion, semantic search, and concept universe visualization powered by local LLMs via LM Studio. Architecture: - Go daemon (17MB single binary, zero dependencies) - chi router, pure-Go SQLite, tiktoken tokenizer - 6-stage pipeline: scan → extract → chunk → embed → annotate → conceptualize - Brute-force cosine vector search in memory - 89 tests across 8 packages - SwiftUI app (macOS 15+) - Multi-workspace management with auto-start daemons - Live pipeline progress, search, concept browser - WebGPU 3D universe renderer with Canvas2D fallback - Custom crystal app icon
44 lines
965 B
Go
44 lines
965 B
Go
package mathutil
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import "math"
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// CosineSimilarity computes cosine similarity between two vectors.
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func CosineSimilarity(a, b []float32) float64 {
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var dot, normA, normB float64
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for i := range a {
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dot += float64(a[i]) * float64(b[i])
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normA += float64(a[i]) * float64(a[i])
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normB += float64(b[i]) * float64(b[i])
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}
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norm := math.Sqrt(normA) * math.Sqrt(normB)
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if norm == 0 {
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return 0
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}
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return dot / norm
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}
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// Normalize normalizes a vector to unit length.
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func Normalize(v []float32) []float32 {
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var norm float64
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for _, x := range v {
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norm += float64(x) * float64(x)
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}
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norm = math.Sqrt(norm)
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if norm == 0 {
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return make([]float32, len(v))
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}
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out := make([]float32, len(v))
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for i, x := range v {
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out[i] = float32(float64(x) / norm)
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}
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return out
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}
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// DotProduct computes the dot product of two vectors.
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func DotProduct(a, b []float32) float64 {
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var sum float64
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for i := range a {
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sum += float64(a[i]) * float64(b[i])
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}
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return sum
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}
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