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/vector/sqlitevec/helpers.go

@@ -12,17 +12,17 @@ const (
 
 // Document represents a document to store with vector embedding.
 type Document struct {
+	Metadata map[string]any
 	ID       string
 	Content  string
-	Metadata map[string]any
 }
 
 // QueryResult represents a search result from vector search.
 type QueryResult struct {
+	Metadata   map[string]any
 	ID         string
 	Distance   float64
-	Similarity float64 // 1.0 = identical, 0.0 = opposite (derived from distance)
-	Metadata   map[string]any
+	Similarity float64
 }
 
 // DistanceToSimilarity converts sqlite-vec cosine distance to similarity score.