Files
claude-mnemonic/internal/vector/hybrid/client_test.go
lukaszraczylo 1ae8035470 feat(graph): add observation graph with hybrid vector storage
- [x] Add golangci.yml configuration with fieldalignment linter
- [x] Implement observation graph structure with edge detection
- [x] Add LEANN-inspired hybrid vector storage with hub threshold
- [x] Implement graph-aware search with selective recomputation
- [x] Add auto-tuner for dynamic hub threshold adjustment
- [x] Add graph and vector metrics tracking and reporting
- [x] Extend configuration for graph parameters
- [x] Add graph rebuild background service with periodic updates
- [x] Add HTTP endpoints for graph stats and vector metrics
- [x] Update UI with advanced metrics sidebar panel
- [x] Implement AST-aware code chunking for Go, Python, TypeScript
2026-01-07 18:51:40 +00:00

187 lines
4.3 KiB
Go

package hybrid
import (
"testing"
"github.com/lukaszraczylo/claude-mnemonic/internal/vector/sqlitevec"
"github.com/stretchr/testify/assert"
)
func TestParseStrategy(t *testing.T) {
tests := []struct {
name string
input string
expected VectorStorageStrategy
}{
{"hub_strategy", "hub", StorageHub},
{"on_demand_strategy", "on_demand", StorageOnDemand},
{"always_strategy", "always", StorageAlways},
{"invalid_defaults_to_hub", "invalid", StorageHub},
{"empty_defaults_to_hub", "", StorageHub},
}
for _, tt := range tests {
t.Run(tt.name, func(t *testing.T) {
result := ParseStrategy(tt.input)
assert.Equal(t, tt.expected, result)
})
}
}
func TestStrategyToString(t *testing.T) {
tests := []struct {
name string
expected string
input VectorStorageStrategy
}{
{"hub_to_string", "hub", StorageHub},
{"on_demand_to_string", "on_demand", StorageOnDemand},
{"always_to_string", "always", StorageAlways},
{"invalid_to_unknown", "unknown", VectorStorageStrategy(99)},
}
for _, tt := range tests {
t.Run(tt.name, func(t *testing.T) {
result := strategyToString(tt.input)
assert.Equal(t, tt.expected, result)
})
}
}
func TestCosineSimilarity(t *testing.T) {
tests := []struct {
name string
a []float32
b []float32
expected float32
}{
{
name: "identical_vectors",
a: []float32{1, 0, 0},
b: []float32{1, 0, 0},
expected: 1.0,
},
{
name: "orthogonal_vectors",
a: []float32{1, 0, 0},
b: []float32{0, 1, 0},
expected: 0.0,
},
{
name: "opposite_vectors",
a: []float32{1, 0, 0},
b: []float32{-1, 0, 0},
expected: -1.0,
},
{
name: "zero_vector",
a: []float32{0, 0, 0},
b: []float32{1, 1, 1},
expected: 0.0,
},
{
name: "parallel_vectors",
a: []float32{2, 0, 0},
b: []float32{4, 0, 0},
expected: 1.0,
},
}
for _, tt := range tests {
t.Run(tt.name, func(t *testing.T) {
result := cosineSimilarity(tt.a, tt.b)
assert.InDelta(t, tt.expected, result, 0.001)
})
}
}
func TestSortBySimilarity(t *testing.T) {
tests := []struct {
name string
input []sqlitevec.QueryResult
expected []string // Expected order of IDs
}{
{
name: "already_sorted",
input: []sqlitevec.QueryResult{
{ID: "doc1", Similarity: 0.9},
{ID: "doc2", Similarity: 0.7},
{ID: "doc3", Similarity: 0.5},
},
expected: []string{"doc1", "doc2", "doc3"},
},
{
name: "reverse_sorted",
input: []sqlitevec.QueryResult{
{ID: "doc1", Similarity: 0.3},
{ID: "doc2", Similarity: 0.7},
{ID: "doc3", Similarity: 0.9},
},
expected: []string{"doc3", "doc2", "doc1"},
},
{
name: "random_order",
input: []sqlitevec.QueryResult{
{ID: "doc1", Similarity: 0.5},
{ID: "doc2", Similarity: 0.9},
{ID: "doc3", Similarity: 0.3},
{ID: "doc4", Similarity: 0.7},
},
expected: []string{"doc2", "doc4", "doc1", "doc3"},
},
{
name: "identical_similarities",
input: []sqlitevec.QueryResult{
{ID: "doc1", Similarity: 0.5},
{ID: "doc2", Similarity: 0.5},
{ID: "doc3", Similarity: 0.5},
},
expected: []string{"doc1", "doc2", "doc3"},
},
{
name: "empty_list",
input: []sqlitevec.QueryResult{},
expected: []string{},
},
{
name: "single_element",
input: []sqlitevec.QueryResult{
{ID: "doc1", Similarity: 0.5},
},
expected: []string{"doc1"},
},
}
for _, tt := range tests {
t.Run(tt.name, func(t *testing.T) {
sortBySimilarity(tt.input)
actual := make([]string, len(tt.input))
for i, r := range tt.input {
actual[i] = r.ID
}
assert.Equal(t, tt.expected, actual)
})
}
}
func TestSortBySimilarity_PreserveOtherFields(t *testing.T) {
input := []sqlitevec.QueryResult{
{ID: "doc1", Similarity: 0.3, Distance: 0.7, Metadata: map[string]any{"key": "val1"}},
{ID: "doc2", Similarity: 0.9, Distance: 0.1, Metadata: map[string]any{"key": "val2"}},
}
sortBySimilarity(input)
assert.Equal(t, "doc2", input[0].ID)
assert.InDelta(t, 0.9, input[0].Similarity, 0.001)
assert.InDelta(t, 0.1, input[0].Distance, 0.001)
assert.Equal(t, "val2", input[0].Metadata["key"])
assert.Equal(t, "doc1", input[1].ID)
assert.InDelta(t, 0.3, input[1].Similarity, 0.001)
assert.InDelta(t, 0.7, input[1].Distance, 0.001)
assert.Equal(t, "val1", input[1].Metadata["key"])
}