feat(leann-phase2): implement hybrid vector storage and graph-based search

- [x] Add AST-aware code chunking for Go, Python, and TypeScript using tree-sitter
- [x] Implement LEANN-inspired hybrid vector storage with hub detection and selective embedding storage (60-80% savings)
- [x] Add observation relationship graph with CSR format and edge detection (file overlap, semantic similarity, temporal, concept)
- [x] Implement graph-aware search with two-level traversal and relationship-based ranking
- [x] Add auto-tuning system for dynamic hub threshold adjustment based on query performance
- [x] Add comprehensive metrics tracking for vector storage, queries, latency, and graph traversals
- [x] Update configuration system with graph and hybrid storage settings
- [x] Add graph stats and vector metrics endpoints to worker service
- [x] Enhance UI sidebar with advanced metrics display and graph visualization
- [x] Optimize struct field alignment throughout codebase for memory efficiency
- [x] Update documentation with LEANN Phase 2 features and performance benefits
- [x] Add tree-sitter dependency for AST parsing

internal/embedding/model.go

@@ -21,15 +21,10 @@ const (
 // ONNXConfig describes ONNX-specific model configuration.
 // This allows different models to specify their tensor names and pooling needs.
 type ONNXConfig struct {
-	// InputNames are the ONNX input tensor names in order.
-	InputNames []string
-	// OutputNames are the ONNX output tensor names.
+	Pooling     PoolingStrategy
+	InputNames  []string
 	OutputNames []string
-	// Pooling specifies how to convert token embeddings to sentence embeddings.
-	// If PoolingNone, the model outputs sentence embeddings directly.
-	Pooling PoolingStrategy
-	// HiddenSize is the embedding dimension (used for pooling calculations).
-	HiddenSize int
+	HiddenSize  int
 }
 
 // EmbeddingModel represents a text embedding model.
@@ -62,11 +57,11 @@ type ONNXConfigurer interface {
 
 // ModelMetadata describes an embedding model for UI/config.
 type ModelMetadata struct {
-	Name        string `json:"name"`        // Human-readable name
-	Version     string `json:"version"`     // Short ID for DB storage
-	Dimensions  int    `json:"dimensions"`  // Vector size
-	Description string `json:"description"` // Brief description
-	Default     bool   `json:"default"`     // Is this the default model?
+	Name        string `json:"name"`
+	Version     string `json:"version"`
+	Description string `json:"description"`
+	Dimensions  int    `json:"dimensions"`
+	Default     bool   `json:"default"`
 }
 
 // ModelFactory creates a new instance of an embedding model.
@@ -74,10 +69,10 @@ type ModelFactory func() (EmbeddingModel, error)
 
 // ModelRegistry provides model lookup by version.
 type ModelRegistry struct {
-	mu           sync.RWMutex
 	models       map[string]ModelFactory
 	metadata     map[string]ModelMetadata
 	defaultModel string
+	mu           sync.RWMutex
 }
 
 // NewModelRegistry creates a new model registry.

internal/embedding/service.go

@@ -46,9 +46,9 @@ var bgeONNXConfig = ONNXConfig{
 type bgeModel struct {
 	tk      *tokenizer.Tokenizer
 	session *ort.DynamicAdvancedSession
+	libDir  string
+	config  ONNXConfig
 	mu      sync.Mutex
-	libDir  string     // temp directory containing extracted libraries
-	config  ONNXConfig // ONNX configuration for this model
 }
 
 // Compile-time check that bgeModel implements EmbeddingModel