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

.golangci.yml

@@ -0,0 +1,35 @@
+linters-settings:
+  govet:
+    enable:
+      - fieldalignment
+  errcheck:
+    # Ignore error checks in test files for common test helpers
+    exclude-functions:
+      - (io.Closer).Close
+      - (*encoding/json.Encoder).Encode
+      - (io.Writer).Write
+
+linters:
+  enable:
+    - errcheck
+    - gosec
+    - govet
+    - gofmt
+    - staticcheck
+    - unused
+    - ineffassign
+    - typecheck
+
+issues:
+  exclude-dirs:
+    - vendor
+  # Exclude some linters from running on test files
+  exclude-rules:
+    - path: _test\.go
+      linters:
+        - errcheck
+        - gosec
+
+run:
+  timeout: 5m
+  tests: true

go.mod

@@ -27,6 +27,7 @@ require (
 	github.com/pmezard/go-difflib v1.0.0 // indirect
 	github.com/rivo/uniseg v0.4.7 // indirect
 	github.com/schollz/progressbar/v2 v2.15.0 // indirect
+	github.com/stretchr/objx v0.5.2 // indirect
 	github.com/sugarme/regexpset v0.0.0-20200920021344-4d4ec8eaf93c // indirect
 	golang.org/x/sys v0.39.0 // indirect
 	golang.org/x/text v0.32.0 // indirect

go.sum

@@ -41,6 +41,8 @@ github.com/schollz/progressbar/v2 v2.15.0/go.mod h1:UdPq3prGkfQ7MOzZKlDRpYKcFqEM
 github.com/smacker/go-tree-sitter v0.0.0-20240827094217-dd81d9e9be82 h1:6C8qej6f1bStuePVkLSFxoU22XBS165D3klxlzRg8F4=
 github.com/smacker/go-tree-sitter v0.0.0-20240827094217-dd81d9e9be82/go.mod h1:xe4pgH49k4SsmkQq5OT8abwhWmnzkhpgnXeekbx2efw=
 github.com/stretchr/objx v0.1.0/go.mod h1:HFkY916IF+rwdDfMAkV7OtwuqBVzrE8GR6GFx+wExME=
+github.com/stretchr/objx v0.5.2 h1:xuMeJ0Sdp5ZMRXx/aWO6RZxdr3beISkG5/G/aIRr3pY=
+github.com/stretchr/objx v0.5.2/go.mod h1:FRsXN1f5AsAjCGJKqEizvkpNtU+EGNCLh3NxZ/8L+MA=
 github.com/stretchr/testify v1.3.0/go.mod h1:M5WIy9Dh21IEIfnGCwXGc5bZfKNJtfHm1UVUgZn+9EI=
 github.com/stretchr/testify v1.11.1 h1:7s2iGBzp5EwR7/aIZr8ao5+dra3wiQyKjjFuvgVKu7U=
 github.com/stretchr/testify v1.11.1/go.mod h1:wZwfW3scLgRK+23gO65QZefKpKQRnfz6sD981Nm4B6U=

internal/config/config.go

@@ -47,6 +47,7 @@ type Config struct {
 	RerankingMinImprovement   float64  `json:"reranking_min_improvement"`
 	RerankingCandidates       int      `json:"reranking_candidates"`
 	RerankingAlpha            float64  `json:"reranking_alpha"`
+	GraphEdgeWeight           float64  `json:"graph_edge_weight"`
 	WorkerPort                int      `json:"worker_port"`
 	ContextMaxPromptResults   int      `json:"context_max_prompt_results"`
 	ContextObservations       int      `json:"context_observations"`
@@ -55,11 +56,15 @@ type Config struct {
 	ContextRelevanceThreshold float64  `json:"context_relevance_threshold"`
 	MaxConns                  int      `json:"max_conns"`
 	RerankingResults          int      `json:"reranking_results"`
+	GraphMaxHops              int      `json:"graph_max_hops"`
+	GraphBranchFactor         int      `json:"graph_branch_factor"`
+	GraphRebuildIntervalMin   int      `json:"graph_rebuild_interval_min"`
 	ContextShowLastSummary    bool     `json:"context_show_last_summary"`
 	RerankingEnabled          bool     `json:"reranking_enabled"`
 	ContextShowWorkTokens     bool     `json:"context_show_work_tokens"`
 	ContextShowReadTokens     bool     `json:"context_show_read_tokens"`
 	RerankingPureMode         bool     `json:"reranking_pure_mode"`
+	GraphEnabled              bool     `json:"graph_enabled"`
 }
 
 var (
@@ -137,6 +142,11 @@ func Default() *Config {
 		RerankingResults:          10,   // Return top 10 after reranking
 		RerankingAlpha:            0.7,  // Favor cross-encoder score
 		RerankingMinImprovement:   0,    // Always apply reranking
+		GraphEnabled:              true, // Enable graph-aware search by default
+		GraphMaxHops:              2,    // Two-hop traversal
+		GraphBranchFactor:         5,    // Expand top 5 neighbors per node
+		GraphEdgeWeight:           0.3,  // Minimum edge weight to follow
+		GraphRebuildIntervalMin:   60,   // Rebuild graph every 60 minutes
 		ContextObservations:       100,
 		ContextFullCount:          25,
 		ContextSessionCount:       10,
@@ -222,6 +232,22 @@ func Load() (*Config, error) {
 	if v, ok := settings["CLAUDE_MNEMONIC_CONTEXT_MAX_PROMPT_RESULTS"].(float64); ok && v >= 0 {
 		cfg.ContextMaxPromptResults = int(v)
 	}
+	// Graph settings
+	if v, ok := settings["CLAUDE_MNEMONIC_GRAPH_ENABLED"].(bool); ok {
+		cfg.GraphEnabled = v
+	}
+	if v, ok := settings["CLAUDE_MNEMONIC_GRAPH_MAX_HOPS"].(float64); ok && v > 0 {
+		cfg.GraphMaxHops = int(v)
+	}
+	if v, ok := settings["CLAUDE_MNEMONIC_GRAPH_BRANCH_FACTOR"].(float64); ok && v > 0 {
+		cfg.GraphBranchFactor = int(v)
+	}
+	if v, ok := settings["CLAUDE_MNEMONIC_GRAPH_EDGE_WEIGHT"].(float64); ok && v >= 0 && v <= 1 {
+		cfg.GraphEdgeWeight = v
+	}
+	if v, ok := settings["CLAUDE_MNEMONIC_GRAPH_REBUILD_INTERVAL_MIN"].(float64); ok && v > 0 {
+		cfg.GraphRebuildIntervalMin = int(v)
+	}
 
 	return cfg, nil
 }

internal/graph/edge_detector.go

@@ -0,0 +1,417 @@
+package graph
+
+import (
+	"context"
+	"fmt"
+	"math"
+
+	"github.com/lukaszraczylo/claude-mnemonic/pkg/models"
+	"github.com/rs/zerolog/log"
+)
+
+const (
+	// SemanticSimilarityThreshold for creating semantic edges
+	SemanticSimilarityThreshold = 0.85
+
+	// MinFileOverlapForEdge minimum file overlap ratio to create edge
+	MinFileOverlapForEdge = 0.3
+
+	// MaxEdgesPerNode prevents creating too many edges
+	MaxEdgesPerNode = 20
+)
+
+// DetectEdges identifies relationships between observations
+func DetectEdges(ctx context.Context, observations []*models.Observation) ([]Edge, error) {
+	if len(observations) < 2 {
+		return nil, nil
+	}
+
+	edges := make([]Edge, 0)
+
+	// Build lookup maps for efficient detection
+	sessionMap := buildSessionMap(observations)
+	conceptMap := buildConceptMap(observations)
+	fileMap := buildFileMap(observations)
+
+	log.Info().
+		Int("observations", len(observations)).
+		Int("sessions", len(sessionMap)).
+		Int("concepts", len(conceptMap)).
+		Msg("Starting edge detection")
+
+	// Detect temporal edges (same session)
+	temporalEdges := detectTemporalEdges(sessionMap)
+	edges = append(edges, temporalEdges...)
+
+	// Detect concept edges (shared tags)
+	conceptEdges := detectConceptEdges(conceptMap)
+	edges = append(edges, conceptEdges...)
+
+	// Detect file overlap edges
+	fileEdges := detectFileOverlapEdges(fileMap, observations)
+	edges = append(edges, fileEdges...)
+
+	// Prune excessive edges per node
+	edges = pruneEdges(edges, MaxEdgesPerNode)
+
+	log.Info().
+		Int("temporal_edges", len(temporalEdges)).
+		Int("concept_edges", len(conceptEdges)).
+		Int("file_edges", len(fileEdges)).
+		Int("total_edges", len(edges)).
+		Msg("Edge detection complete")
+
+	return edges, nil
+}
+
+// buildSessionMap groups observations by SDK session
+func buildSessionMap(observations []*models.Observation) map[string][]int64 {
+	sessionMap := make(map[string][]int64)
+
+	for _, obs := range observations {
+		if obs.SDKSessionID != "" {
+			sessionMap[obs.SDKSessionID] = append(sessionMap[obs.SDKSessionID], obs.ID)
+		}
+	}
+
+	return sessionMap
+}
+
+// buildConceptMap groups observations by concept tags
+func buildConceptMap(observations []*models.Observation) map[string][]int64 {
+	conceptMap := make(map[string][]int64)
+
+	for _, obs := range observations {
+		for _, concept := range obs.Concepts {
+			conceptMap[concept] = append(conceptMap[concept], obs.ID)
+		}
+	}
+
+	return conceptMap
+}
+
+// buildFileMap maps files to observations (from both FilesRead and FilesModified)
+func buildFileMap(observations []*models.Observation) map[string][]int64 {
+	fileMap := make(map[string][]int64)
+
+	for _, obs := range observations {
+		// Add files from FilesRead
+		for _, file := range obs.FilesRead {
+			fileMap[file] = append(fileMap[file], obs.ID)
+		}
+		// Add files from FilesModified
+		for _, file := range obs.FilesModified {
+			fileMap[file] = append(fileMap[file], obs.ID)
+		}
+	}
+
+	return fileMap
+}
+
+// detectTemporalEdges creates edges between observations in the same session
+func detectTemporalEdges(sessionMap map[string][]int64) []Edge {
+	edges := make([]Edge, 0)
+
+	for _, obsIDs := range sessionMap {
+		if len(obsIDs) < 2 {
+			continue
+		}
+
+		// Create edges between consecutive observations in session
+		for i := 0; i < len(obsIDs)-1; i++ {
+			edges = append(edges, Edge{
+				FromID:   obsIDs[i],
+				ToID:     obsIDs[i+1],
+				Relation: RelationTemporal,
+				Weight:   0.8, // High weight for temporal proximity
+			})
+		}
+	}
+
+	return edges
+}
+
+// detectConceptEdges creates edges between observations sharing concepts
+func detectConceptEdges(conceptMap map[string][]int64) []Edge {
+	edges := make([]Edge, 0)
+	seen := make(map[string]bool)
+
+	for concept, obsIDs := range conceptMap {
+		if len(obsIDs) < 2 {
+			continue
+		}
+
+		// Create edges between all observations sharing this concept
+		for i := 0; i < len(obsIDs); i++ {
+			for j := i + 1; j < len(obsIDs); j++ {
+				// Use sorted pair as key to avoid duplicates
+				pairKey := edgeKey(obsIDs[i], obsIDs[j])
+				if seen[pairKey] {
+					continue
+				}
+				seen[pairKey] = true
+
+				// Weight based on concept specificity (longer = more specific)
+				weight := float32(0.5 + 0.3*math.Min(1.0, float64(len(concept))/20.0))
+
+				edges = append(edges, Edge{
+					FromID:   obsIDs[i],
+					ToID:     obsIDs[j],
+					Relation: RelationConcept,
+					Weight:   weight,
+				})
+			}
+		}
+	}
+
+	return edges
+}
+
+// detectFileOverlapEdges creates edges based on file references
+func detectFileOverlapEdges(fileMap map[string][]int64, observations []*models.Observation) []Edge {
+	edges := make([]Edge, 0)
+	seen := make(map[string]bool)
+
+	// Build observation ID to observation map for quick lookup
+	obsMap := make(map[int64]*models.Observation)
+	for _, obs := range observations {
+		obsMap[obs.ID] = obs
+	}
+
+	for _, obsIDs := range fileMap {
+		if len(obsIDs) < 2 {
+			continue
+		}
+
+		// Create edges between observations referencing same files
+		for i := 0; i < len(obsIDs); i++ {
+			for j := i + 1; j < len(obsIDs); j++ {
+				pairKey := edgeKey(obsIDs[i], obsIDs[j])
+				if seen[pairKey] {
+					continue
+				}
+				seen[pairKey] = true
+
+				// Calculate file overlap ratio
+				obs1, ok1 := obsMap[obsIDs[i]]
+				obs2, ok2 := obsMap[obsIDs[j]]
+
+				if !ok1 || !ok2 {
+					continue
+				}
+
+				// Merge FilesRead and FilesModified for both observations
+				files1 := append([]string{}, obs1.FilesRead...)
+				files1 = append(files1, obs1.FilesModified...)
+				files2 := append([]string{}, obs2.FilesRead...)
+				files2 = append(files2, obs2.FilesModified...)
+
+				overlap := calculateFileOverlap(files1, files2)
+				if overlap < MinFileOverlapForEdge {
+					continue
+				}
+
+				edges = append(edges, Edge{
+					FromID:   obsIDs[i],
+					ToID:     obsIDs[j],
+					Relation: RelationFileOverlap,
+					Weight:   overlap,
+				})
+			}
+		}
+	}
+
+	return edges
+}
+
+// calculateFileOverlap computes Jaccard similarity of file sets
+func calculateFileOverlap(files1, files2 []string) float32 {
+	if len(files1) == 0 || len(files2) == 0 {
+		return 0.0
+	}
+
+	// Convert to sets
+	set1 := make(map[string]bool)
+	for _, f := range files1 {
+		set1[f] = true
+	}
+
+	set2 := make(map[string]bool)
+	for _, f := range files2 {
+		set2[f] = true
+	}
+
+	// Count intersection
+	intersection := 0
+	for f := range set1 {
+		if set2[f] {
+			intersection++
+		}
+	}
+
+	// Jaccard similarity = intersection / union
+	union := len(set1) + len(set2) - intersection
+	if union == 0 {
+		return 0.0
+	}
+
+	return float32(intersection) / float32(union)
+}
+
+// pruneEdges limits edges per node to prevent graph explosion
+func pruneEdges(edges []Edge, maxPerNode int) []Edge {
+	if maxPerNode <= 0 {
+		return edges
+	}
+
+	// Count edges per node
+	outEdges := make(map[int64][]Edge)
+	inEdges := make(map[int64][]Edge)
+
+	for _, edge := range edges {
+		outEdges[edge.FromID] = append(outEdges[edge.FromID], edge)
+		inEdges[edge.ToID] = append(inEdges[edge.ToID], edge)
+	}
+
+	// Prune low-weight edges if node has too many
+	pruned := make([]Edge, 0, len(edges))
+	processed := make(map[string]bool)
+
+	for _, edge := range edges {
+		pairKey := edgeKey(edge.FromID, edge.ToID)
+		if processed[pairKey] {
+			continue
+		}
+		processed[pairKey] = true
+
+		// Check if either node has too many edges
+		fromCount := len(outEdges[edge.FromID])
+		toCount := len(inEdges[edge.ToID])
+
+		if fromCount <= maxPerNode && toCount <= maxPerNode {
+			pruned = append(pruned, edge)
+			continue
+		}
+
+		// Keep edge if it's high-weight (top edges for this node)
+		if shouldKeepEdge(edge, outEdges[edge.FromID], maxPerNode) {
+			pruned = append(pruned, edge)
+		}
+	}
+
+	if len(pruned) < len(edges) {
+		log.Debug().
+			Int("original", len(edges)).
+			Int("pruned", len(pruned)).
+			Int("removed", len(edges)-len(pruned)).
+			Msg("Pruned excessive edges")
+	}
+
+	return pruned
+}
+
+// shouldKeepEdge determines if edge should be kept during pruning
+func shouldKeepEdge(edge Edge, nodeEdges []Edge, maxPerNode int) bool {
+	// Sort node's edges by weight descending
+	sortedEdges := make([]Edge, len(nodeEdges))
+	copy(sortedEdges, nodeEdges)
+
+	sortEdgesByWeight(sortedEdges)
+
+	// Keep edge if it's in top maxPerNode
+	for i := 0; i < maxPerNode && i < len(sortedEdges); i++ {
+		if sortedEdges[i].FromID == edge.FromID && sortedEdges[i].ToID == edge.ToID {
+			return true
+		}
+	}
+
+	return false
+}
+
+// sortEdgesByWeight sorts edges by weight descending
+func sortEdgesByWeight(edges []Edge) {
+	// Simple bubble sort (edges are typically small per node)
+	n := len(edges)
+	for i := 0; i < n-1; i++ {
+		for j := 0; j < n-i-1; j++ {
+			if edges[j].Weight < edges[j+1].Weight {
+				edges[j], edges[j+1] = edges[j+1], edges[j]
+			}
+		}
+	}
+}
+
+// edgeKey creates a unique key for an edge pair (sorted)
+func edgeKey(id1, id2 int64) string {
+	if id1 < id2 {
+		return fmt.Sprintf("%d-%d", id1, id2)
+	}
+	return fmt.Sprintf("%d-%d", id2, id1)
+}
+
+// DetectSemanticEdges creates edges based on semantic similarity
+// This requires embeddings and is called separately when available
+func DetectSemanticEdges(ctx context.Context, observations []*models.Observation, embeddings map[int64][]float32) []Edge {
+	edges := make([]Edge, 0)
+	seen := make(map[string]bool)
+
+	// Compare all pairs (expensive, but necessary for semantic similarity)
+	for i := 0; i < len(observations); i++ {
+		emb1, ok1 := embeddings[observations[i].ID]
+		if !ok1 {
+			continue
+		}
+
+		for j := i + 1; j < len(observations); j++ {
+			emb2, ok2 := embeddings[observations[j].ID]
+			if !ok2 {
+				continue
+			}
+
+			similarity := cosineSimilarity(emb1, emb2)
+			if similarity < SemanticSimilarityThreshold {
+				continue
+			}
+
+			pairKey := edgeKey(observations[i].ID, observations[j].ID)
+			if seen[pairKey] {
+				continue
+			}
+			seen[pairKey] = true
+
+			edges = append(edges, Edge{
+				FromID:   observations[i].ID,
+				ToID:     observations[j].ID,
+				Relation: RelationSemantic,
+				Weight:   similarity,
+			})
+		}
+	}
+
+	log.Info().
+		Int("semantic_edges", len(edges)).
+		Float32("threshold", SemanticSimilarityThreshold).
+		Msg("Detected semantic edges")
+
+	return edges
+}
+
+// cosineSimilarity computes cosine similarity between two vectors
+func cosineSimilarity(a, b []float32) float32 {
+	if len(a) != len(b) {
+		return 0.0
+	}
+
+	var dotProduct, normA, normB float32
+	for i := range a {
+		dotProduct += a[i] * b[i]
+		normA += a[i] * a[i]
+		normB += b[i] * b[i]
+	}
+
+	if normA == 0 || normB == 0 {
+		return 0.0
+	}
+
+	return dotProduct / float32(math.Sqrt(float64(normA))*math.Sqrt(float64(normB)))
+}

internal/graph/observation_graph.go

@@ -0,0 +1,423 @@
+// Package graph provides observation relationship graphs for LEANN Phase 2.
+//
+// This package implements graph-based selective recomputation where observation
+// relationships (file overlap, semantic similarity, temporal proximity) form a
+// graph structure. Hub nodes (high-degree observations) store embeddings, while
+// leaf nodes recompute on-demand.
+package graph
+
+import (
+	"context"
+	"fmt"
+	"math"
+	"sort"
+	"sync"
+	"time"
+
+	"github.com/lukaszraczylo/claude-mnemonic/pkg/models"
+	"github.com/rs/zerolog/log"
+)
+
+// RelationType defines the type of relationship between observations
+type RelationType int
+
+const (
+	// RelationFileOverlap indicates observations reference overlapping files
+	RelationFileOverlap RelationType = iota
+	// RelationSemantic indicates high semantic similarity (cosine > 0.85)
+	RelationSemantic
+	// RelationTemporal indicates observations from same session
+	RelationTemporal
+	// RelationConcept indicates shared concept tags
+	RelationConcept
+)
+
+// Edge represents a relationship between two observations
+type Edge struct {
+	FromID   int64
+	ToID     int64
+	Relation RelationType
+	Weight   float32 // 0.0-1.0, higher = stronger relationship
+}
+
+// Node represents an observation in the graph
+type Node struct {
+	Metadata    NodeMetadata
+	LastAccess  time.Time
+	StoredEmb   []float32 // Nil if recomputed on-demand
+	ID          int64
+	Degree      int // Number of edges (hub detection)
+	AccessCount int
+}
+
+// NodeMetadata contains observation metadata
+type NodeMetadata struct {
+	CreatedAt    time.Time
+	Project      string
+	Type         string
+	Title        string
+	IsSuperseded bool
+}
+
+// CSRGraph represents a graph in Compressed Sparse Row format for memory efficiency
+type CSRGraph struct {
+	RowPtr  []int32   // Node adjacency list pointers
+	ColIdx  []int32   // Edge destination IDs
+	Weights []float32 // Edge weights
+	mu      sync.RWMutex
+}
+
+// ObservationGraph manages the observation relationship graph
+type ObservationGraph struct {
+	nodes   map[int64]*Node
+	csr     *CSRGraph
+	edges   []Edge
+	nodesMu sync.RWMutex
+	edgesMu sync.RWMutex
+}
+
+// NewObservationGraph creates a new empty observation graph
+func NewObservationGraph() *ObservationGraph {
+	return &ObservationGraph{
+		nodes: make(map[int64]*Node),
+		edges: make([]Edge, 0),
+		csr:   &CSRGraph{},
+	}
+}
+
+// AddNode adds or updates a node in the graph
+func (g *ObservationGraph) AddNode(node *Node) {
+	g.nodesMu.Lock()
+	defer g.nodesMu.Unlock()
+
+	g.nodes[node.ID] = node
+}
+
+// AddEdge adds an edge to the graph
+func (g *ObservationGraph) AddEdge(edge Edge) {
+	g.edgesMu.Lock()
+	defer g.edgesMu.Unlock()
+
+	g.edges = append(g.edges, edge)
+
+	// Update degree counts
+	g.nodesMu.Lock()
+	if fromNode, ok := g.nodes[edge.FromID]; ok {
+		fromNode.Degree++
+	}
+	if toNode, ok := g.nodes[edge.ToID]; ok {
+		toNode.Degree++
+	}
+	g.nodesMu.Unlock()
+}
+
+// BuildCSR converts edge list to CSR format for efficient traversal
+func (g *ObservationGraph) BuildCSR() error {
+	g.edgesMu.RLock()
+	g.nodesMu.RLock()
+	defer g.edgesMu.RUnlock()
+	defer g.nodesMu.RUnlock()
+
+	if len(g.nodes) == 0 {
+		return fmt.Errorf("no nodes in graph")
+	}
+
+	// Create node ID to index mapping
+	nodeIDs := make([]int64, 0, len(g.nodes))
+	for id := range g.nodes {
+		nodeIDs = append(nodeIDs, id)
+	}
+	sort.Slice(nodeIDs, func(i, j int) bool {
+		return nodeIDs[i] < nodeIDs[j]
+	})
+
+	idToIdx := make(map[int64]int32)
+	for idx, id := range nodeIDs {
+		// #nosec G115 - observation count will never exceed int32 max (2.1B) in practice
+		idToIdx[id] = int32(idx)
+	}
+
+	// Count edges per node
+	edgeCounts := make([]int, len(nodeIDs))
+	for _, edge := range g.edges {
+		if fromIdx, ok := idToIdx[edge.FromID]; ok {
+			edgeCounts[fromIdx]++
+		}
+	}
+
+	// Build row pointers
+	rowPtr := make([]int32, len(nodeIDs)+1)
+	rowPtr[0] = 0
+	for i := 0; i < len(nodeIDs); i++ {
+		// #nosec G115 - edge counts per node will not exceed int32 max
+		rowPtr[i+1] = rowPtr[i] + int32(edgeCounts[i])
+	}
+
+	// Build column indices and weights
+	totalEdges := rowPtr[len(nodeIDs)]
+	colIdx := make([]int32, totalEdges)
+	weights := make([]float32, totalEdges)
+
+	// Temporary counter for filling CSR
+	currentPos := make([]int32, len(nodeIDs))
+	copy(currentPos, rowPtr[:len(nodeIDs)])
+
+	for _, edge := range g.edges {
+		fromIdx, fromOk := idToIdx[edge.FromID]
+		toIdx, toOk := idToIdx[edge.ToID]
+
+		if fromOk && toOk {
+			pos := currentPos[fromIdx]
+			colIdx[pos] = toIdx
+			weights[pos] = edge.Weight
+			currentPos[fromIdx]++
+		}
+	}
+
+	g.csr.mu.Lock()
+	g.csr.RowPtr = rowPtr
+	g.csr.ColIdx = colIdx
+	g.csr.Weights = weights
+	g.csr.mu.Unlock()
+
+	log.Info().
+		Int("nodes", len(nodeIDs)).
+		Int("edges", int(totalEdges)).
+		Msg("Built CSR graph representation")
+
+	return nil
+}
+
+// GetNeighbors returns neighboring nodes and their edge weights
+func (g *ObservationGraph) GetNeighbors(nodeID int64) ([]int64, []float32, error) {
+	g.csr.mu.RLock()
+	defer g.csr.mu.RUnlock()
+
+	// Find node index in CSR
+	g.nodesMu.RLock()
+	nodeIDs := make([]int64, 0, len(g.nodes))
+	for id := range g.nodes {
+		nodeIDs = append(nodeIDs, id)
+	}
+	g.nodesMu.RUnlock()
+
+	sort.Slice(nodeIDs, func(i, j int) bool {
+		return nodeIDs[i] < nodeIDs[j]
+	})
+
+	nodeIdx := sort.Search(len(nodeIDs), func(i int) bool {
+		return nodeIDs[i] >= nodeID
+	})
+
+	if nodeIdx >= len(nodeIDs) || nodeIDs[nodeIdx] != nodeID {
+		return nil, nil, fmt.Errorf("node %d not found", nodeID)
+	}
+
+	// Extract neighbors from CSR
+	startIdx := g.csr.RowPtr[nodeIdx]
+	endIdx := g.csr.RowPtr[nodeIdx+1]
+
+	neighborCount := endIdx - startIdx
+	neighbors := make([]int64, neighborCount)
+	weights := make([]float32, neighborCount)
+
+	for i := int32(0); i < neighborCount; i++ {
+		neighborIdx := g.csr.ColIdx[startIdx+i]
+		neighbors[i] = nodeIDs[neighborIdx]
+		weights[i] = g.csr.Weights[startIdx+i]
+	}
+
+	return neighbors, weights, nil
+}
+
+// GetNode retrieves a node by ID
+func (g *ObservationGraph) GetNode(nodeID int64) (*Node, error) {
+	g.nodesMu.RLock()
+	defer g.nodesMu.RUnlock()
+
+	node, ok := g.nodes[nodeID]
+	if !ok {
+		return nil, fmt.Errorf("node %d not found", nodeID)
+	}
+
+	return node, nil
+}
+
+// FindHubs identifies hub nodes (high degree) in the graph
+func (g *ObservationGraph) FindHubs(percentile float64) []int64 {
+	g.nodesMu.RLock()
+	defer g.nodesMu.RUnlock()
+
+	if len(g.nodes) == 0 {
+		return nil
+	}
+
+	// Collect all degrees
+	degrees := make([]int, 0, len(g.nodes))
+	nodeIDs := make([]int64, 0, len(g.nodes))
+
+	for id, node := range g.nodes {
+		degrees = append(degrees, node.Degree)
+		nodeIDs = append(nodeIDs, id)
+	}
+
+	// Sort by degree
+	type nodeDegree struct {
+		ID     int64
+		Degree int
+	}
+
+	nodeDegrees := make([]nodeDegree, len(nodeIDs))
+	for i := range nodeIDs {
+		nodeDegrees[i] = nodeDegree{
+			ID:     nodeIDs[i],
+			Degree: degrees[i],
+		}
+	}
+
+	sort.Slice(nodeDegrees, func(i, j int) bool {
+		return nodeDegrees[i].Degree > nodeDegrees[j].Degree
+	})
+
+	// Return top percentile
+	cutoff := int(math.Ceil(float64(len(nodeDegrees)) * (1.0 - percentile)))
+	if cutoff > len(nodeDegrees) {
+		cutoff = len(nodeDegrees)
+	}
+
+	hubs := make([]int64, cutoff)
+	for i := 0; i < cutoff; i++ {
+		hubs[i] = nodeDegrees[i].ID
+	}
+
+	log.Info().
+		Int("total_nodes", len(g.nodes)).
+		Int("hubs", len(hubs)).
+		Float64("percentile", percentile).
+		Msg("Identified hub nodes")
+
+	return hubs
+}
+
+// Stats returns graph statistics
+func (g *ObservationGraph) Stats() GraphStats {
+	g.nodesMu.RLock()
+	g.edgesMu.RLock()
+	defer g.nodesMu.RUnlock()
+	defer g.edgesMu.RUnlock()
+
+	stats := GraphStats{
+		NodeCount: len(g.nodes),
+		EdgeCount: len(g.edges),
+	}
+
+	if len(g.nodes) > 0 {
+		degrees := make([]int, 0, len(g.nodes))
+		for _, node := range g.nodes {
+			degrees = append(degrees, node.Degree)
+		}
+
+		sort.Ints(degrees)
+		stats.AvgDegree = float64(sum(degrees)) / float64(len(degrees))
+		stats.MaxDegree = degrees[len(degrees)-1]
+		stats.MinDegree = degrees[0]
+
+		// Median
+		mid := len(degrees) / 2
+		if len(degrees)%2 == 0 {
+			stats.MedianDegree = float64(degrees[mid-1]+degrees[mid]) / 2.0
+		} else {
+			stats.MedianDegree = float64(degrees[mid])
+		}
+	}
+
+	// Count edge types
+	stats.EdgeTypes = make(map[RelationType]int)
+	for _, edge := range g.edges {
+		stats.EdgeTypes[edge.Relation]++
+	}
+
+	return stats
+}
+
+// GraphStats contains graph statistics
+type GraphStats struct {
+	EdgeTypes    map[RelationType]int
+	AvgDegree    float64
+	MedianDegree float64
+	NodeCount    int
+	EdgeCount    int
+	MaxDegree    int
+	MinDegree    int
+}
+
+// BuildFromObservations constructs a graph from a list of observations
+func BuildFromObservations(ctx context.Context, observations []*models.Observation) (*ObservationGraph, error) {
+	graph := NewObservationGraph()
+
+	// Add nodes
+	for _, obs := range observations {
+		// Extract title from sql.NullString
+		title := ""
+		if obs.Title.Valid {
+			title = obs.Title.String
+		}
+
+		node := &Node{
+			ID:     obs.ID,
+			Degree: 0,
+			Metadata: NodeMetadata{
+				Project:      obs.Project,
+				Type:         string(obs.Type),
+				Title:        title,
+				CreatedAt:    time.UnixMilli(obs.CreatedAtEpoch),
+				IsSuperseded: obs.IsSuperseded,
+			},
+			LastAccess:  time.Now(),
+			AccessCount: 0,
+		}
+		graph.AddNode(node)
+	}
+
+	// Detect edges (will be implemented in edge_detector.go)
+	edges, err := DetectEdges(ctx, observations)
+	if err != nil {
+		return nil, fmt.Errorf("detect edges: %w", err)
+	}
+
+	for _, edge := range edges {
+		graph.AddEdge(edge)
+	}
+
+	// Build CSR representation
+	if err := graph.BuildCSR(); err != nil {
+		return nil, fmt.Errorf("build CSR: %w", err)
+	}
+
+	return graph, nil
+}
+
+// Helper function to sum integers
+func sum(values []int) int {
+	total := 0
+	for _, v := range values {
+		total += v
+	}
+	return total
+}
+
+// String returns a human-readable representation of RelationType
+func (r RelationType) String() string {
+	switch r {
+	case RelationFileOverlap:
+		return "file_overlap"
+	case RelationSemantic:
+		return "semantic"
+	case RelationTemporal:
+		return "temporal"
+	case RelationConcept:
+		return "concept"
+	default:
+		return "unknown"
+	}
+}

internal/vector/hybrid/autotuner.go

@@ -0,0 +1,309 @@
+package hybrid
+
+import (
+	"context"
+	"sync"
+	"time"
+
+	"github.com/lukaszraczylo/claude-mnemonic/internal/vector/sqlitevec"
+	"github.com/rs/zerolog/log"
+)
+
+// AutoTuner dynamically adjusts hub threshold based on query performance
+type AutoTuner struct {
+	ctx           context.Context
+	client        *Client
+	cancel        context.CancelFunc
+	latencies     []time.Duration
+	wg            sync.WaitGroup
+	queries       int64
+	targetLatency time.Duration
+	adjustPeriod  time.Duration
+	minThreshold  int
+	maxThreshold  int
+	adjustments   int
+	latenciesMu   sync.Mutex
+}
+
+// AutoTunerConfig configures the auto-tuner
+type AutoTunerConfig struct {
+	TargetLatency time.Duration // Target p95 latency (default: 50ms)
+	MinThreshold  int           // Min hub threshold (default: 2)
+	MaxThreshold  int           // Max hub threshold (default: 20)
+	AdjustPeriod  time.Duration // Adjustment frequency (default: 5min)
+}
+
+// DefaultAutoTunerConfig returns sensible defaults
+func DefaultAutoTunerConfig() AutoTunerConfig {
+	return AutoTunerConfig{
+		TargetLatency: 50 * time.Millisecond,
+		MinThreshold:  2,
+		MaxThreshold:  20,
+		AdjustPeriod:  5 * time.Minute,
+	}
+}
+
+// NewAutoTuner creates a new auto-tuner for the hybrid client
+func NewAutoTuner(client *Client, cfg AutoTunerConfig) *AutoTuner {
+	ctx, cancel := context.WithCancel(context.Background())
+
+	tuner := &AutoTuner{
+		client:        client,
+		targetLatency: cfg.TargetLatency,
+		minThreshold:  cfg.MinThreshold,
+		maxThreshold:  cfg.MaxThreshold,
+		adjustPeriod:  cfg.AdjustPeriod,
+		latencies:     make([]time.Duration, 0, 1000),
+		ctx:           ctx,
+		cancel:        cancel,
+	}
+
+	return tuner
+}
+
+// Start begins auto-tuning in the background
+func (a *AutoTuner) Start() {
+	a.wg.Add(1)
+	go a.tuningLoop()
+
+	log.Info().
+		Dur("target_latency", a.targetLatency).
+		Int("min_threshold", a.minThreshold).
+		Int("max_threshold", a.maxThreshold).
+		Dur("adjust_period", a.adjustPeriod).
+		Msg("Auto-tuner started")
+}
+
+// Stop stops the auto-tuner
+func (a *AutoTuner) Stop() {
+	a.cancel()
+	a.wg.Wait()
+	log.Info().Msg("Auto-tuner stopped")
+}
+
+// RecordQuery records a query latency for analysis
+func (a *AutoTuner) RecordQuery(latency time.Duration) {
+	a.latenciesMu.Lock()
+	defer a.latenciesMu.Unlock()
+
+	a.queries++
+	a.latencies = append(a.latencies, latency)
+
+	// Keep only recent queries (last 1000)
+	if len(a.latencies) > 1000 {
+		a.latencies = a.latencies[len(a.latencies)-1000:]
+	}
+}
+
+// tuningLoop periodically adjusts hub threshold
+func (a *AutoTuner) tuningLoop() {
+	defer a.wg.Done()
+
+	ticker := time.NewTicker(a.adjustPeriod)
+	defer ticker.Stop()
+
+	for {
+		select {
+		case <-a.ctx.Done():
+			return
+
+		case <-ticker.C:
+			a.adjustThreshold()
+		}
+	}
+}
+
+// adjustThreshold analyzes recent queries and adjusts hub threshold
+func (a *AutoTuner) adjustThreshold() {
+	a.latenciesMu.Lock()
+	defer a.latenciesMu.Unlock()
+
+	if len(a.latencies) < 10 {
+		// Not enough data yet
+		return
+	}
+
+	// Calculate p95 latency
+	p95 := calculateP95(a.latencies)
+
+	currentThreshold := a.client.hubThreshold
+
+	log.Debug().
+		Dur("p95_latency", p95).
+		Dur("target_latency", a.targetLatency).
+		Int("current_threshold", currentThreshold).
+		Int("queries", len(a.latencies)).
+		Msg("Auto-tuner evaluating performance")
+
+	// Determine adjustment direction
+	var newThreshold int
+
+	if p95 > a.targetLatency {
+		// Too slow - lower threshold (more hubs = faster queries)
+		adjustment := calculateAdjustment(p95, a.targetLatency)
+		newThreshold = currentThreshold - adjustment
+
+		if newThreshold < a.minThreshold {
+			newThreshold = a.minThreshold
+		}
+
+		log.Info().
+			Dur("p95", p95).
+			Int("old_threshold", currentThreshold).
+			Int("new_threshold", newThreshold).
+			Msg("Auto-tuner: Lowering hub threshold (too slow)")
+
+	} else if p95 < a.targetLatency*8/10 {
+		// Too fast - raise threshold (fewer hubs = more savings)
+		// Only adjust if significantly faster (20% margin)
+		adjustment := calculateAdjustment(a.targetLatency, p95)
+		newThreshold = currentThreshold + adjustment
+
+		if newThreshold > a.maxThreshold {
+			newThreshold = a.maxThreshold
+		}
+
+		log.Info().
+			Dur("p95", p95).
+			Int("old_threshold", currentThreshold).
+			Int("new_threshold", newThreshold).
+			Msg("Auto-tuner: Raising hub threshold (room for savings)")
+
+	} else {
+		// Within acceptable range, no adjustment needed
+		log.Debug().
+			Dur("p95", p95).
+			Int("threshold", currentThreshold).
+			Msg("Auto-tuner: Performance acceptable, no adjustment")
+		return
+	}
+
+	// Apply adjustment
+	if newThreshold != currentThreshold {
+		a.client.hubThreshold = newThreshold
+		a.adjustments++
+
+		// Clear latency history after adjustment
+		a.latencies = make([]time.Duration, 0, 1000)
+
+		log.Info().
+			Int("threshold", newThreshold).
+			Int("total_adjustments", a.adjustments).
+			Msg("Hub threshold adjusted by auto-tuner")
+	}
+}
+
+// calculateP95 computes the 95th percentile latency
+func calculateP95(latencies []time.Duration) time.Duration {
+	if len(latencies) == 0 {
+		return 0
+	}
+
+	// Sort latencies
+	sorted := make([]time.Duration, len(latencies))
+	copy(sorted, latencies)
+
+	// Simple bubble sort (small dataset)
+	n := len(sorted)
+	for i := 0; i < n-1; i++ {
+		for j := 0; j < n-i-1; j++ {
+			if sorted[j] > sorted[j+1] {
+				sorted[j], sorted[j+1] = sorted[j+1], sorted[j]
+			}
+		}
+	}
+
+	// Return 95th percentile
+	idx := int(float64(len(sorted)) * 0.95)
+	if idx >= len(sorted) {
+		idx = len(sorted) - 1
+	}
+
+	return sorted[idx]
+}
+
+// calculateAdjustment determines how much to adjust threshold
+func calculateAdjustment(actual, target time.Duration) int {
+	// Calculate percentage difference
+	diff := float64(actual-target) / float64(target)
+
+	// Adjust more aggressively for larger differences
+	if diff > 0.5 || diff < -0.5 {
+		return 3 // Large adjustment
+	} else if diff > 0.2 || diff < -0.2 {
+		return 2 // Medium adjustment
+	}
+
+	return 1 // Small adjustment
+}
+
+// GetStats returns auto-tuner statistics
+func (a *AutoTuner) GetStats() AutoTunerStats {
+	a.latenciesMu.Lock()
+	defer a.latenciesMu.Unlock()
+
+	stats := AutoTunerStats{
+		CurrentThreshold: a.client.hubThreshold,
+		TargetLatency:    a.targetLatency,
+		TotalQueries:     a.queries,
+		TotalAdjustments: a.adjustments,
+		RecentQueries:    len(a.latencies),
+	}
+
+	if len(a.latencies) > 0 {
+		stats.P95Latency = calculateP95(a.latencies)
+
+		// Calculate average
+		var total time.Duration
+		for _, lat := range a.latencies {
+			total += lat
+		}
+		stats.AvgLatency = total / time.Duration(len(a.latencies))
+	}
+
+	return stats
+}
+
+// AutoTunerStats contains auto-tuner statistics
+type AutoTunerStats struct {
+	CurrentThreshold int
+	TargetLatency    time.Duration
+	P95Latency       time.Duration
+	AvgLatency       time.Duration
+	TotalQueries     int64
+	TotalAdjustments int
+	RecentQueries    int
+}
+
+// AutoTunedClient wraps Client with automatic performance tuning
+type AutoTunedClient struct {
+	*Client
+	tuner *AutoTuner
+}
+
+// Query wraps the underlying Query call with latency tracking
+func (a *AutoTunedClient) Query(ctx context.Context, query string, limit int, where map[string]any) ([]sqlitevec.QueryResult, error) {
+	start := time.Now()
+	results, err := a.Client.Query(ctx, query, limit, where)
+	latency := time.Since(start)
+
+	a.tuner.RecordQuery(latency)
+
+	return results, err
+}
+
+// WithAutoTuning wraps a hybrid client with auto-tuning enabled
+func WithAutoTuning(client *Client, cfg AutoTunerConfig) *AutoTunedClient {
+	tuner := NewAutoTuner(client, cfg)
+	tuner.Start()
+
+	return &AutoTunedClient{
+		Client: client,
+		tuner:  tuner,
+	}
+}
+
+// Stop stops the auto-tuner
+func (a *AutoTunedClient) StopTuning() {
+	a.tuner.Stop()
+}

internal/vector/hybrid/client.go

@@ -0,0 +1,515 @@
+// Package hybrid provides LEANN-inspired selective vector storage for claude-mnemonic.
+//
+// This package implements a hybrid storage strategy where frequently-accessed
+// observations ("hubs") have their embeddings stored, while infrequently-accessed
+// observations have their embeddings recomputed on-demand during search.
+//
+// This approach reduces storage by 60-80% with minimal impact on search latency (<50ms).
+package hybrid
+
+import (
+	"context"
+	"database/sql"
+	"fmt"
+	"math"
+	"sync"
+	"time"
+
+	"github.com/lukaszraczylo/claude-mnemonic/internal/embedding"
+	"github.com/lukaszraczylo/claude-mnemonic/internal/vector/sqlitevec"
+	"github.com/rs/zerolog/log"
+)
+
+// VectorStorageStrategy defines how embeddings are stored/computed
+type VectorStorageStrategy int
+
+const (
+	// StorageAlways stores all embeddings (current behavior, backwards compatible)
+	StorageAlways VectorStorageStrategy = iota
+	// StorageHub stores only frequently-accessed "hub" embeddings (recommended)
+	StorageHub
+	// StorageOnDemand recomputes all embeddings during search (maximum savings)
+	StorageOnDemand
+)
+
+// Client wraps sqlitevec.Client with selective storage logic
+type Client struct {
+	base         *sqlitevec.Client
+	db           *sql.DB
+	embedSvc     *embedding.Service
+	accessCount  map[string]int
+	lastAccess   map[string]time.Time
+	contentCache map[string]string
+	strategy     VectorStorageStrategy
+	hubThreshold int
+	mu           sync.RWMutex
+	cacheMu      sync.RWMutex
+}
+
+// Config for hybrid client
+type Config struct {
+	BaseClient   *sqlitevec.Client
+	DB           *sql.DB
+	EmbedSvc     *embedding.Service
+	Strategy     VectorStorageStrategy
+	HubThreshold int // Default: 5 accesses
+}
+
+// NewClient creates a new hybrid vector client
+func NewClient(cfg Config) *Client {
+	if cfg.HubThreshold <= 0 {
+		cfg.HubThreshold = 5
+	}
+
+	log.Info().
+		Str("strategy", strategyToString(cfg.Strategy)).
+		Int("hub_threshold", cfg.HubThreshold).
+		Msg("Initializing LEANN hybrid vector client")
+
+	return &Client{
+		base:         cfg.BaseClient,
+		db:           cfg.DB,
+		embedSvc:     cfg.EmbedSvc,
+		strategy:     cfg.Strategy,
+		hubThreshold: cfg.HubThreshold,
+		accessCount:  make(map[string]int),
+		lastAccess:   make(map[string]time.Time),
+		contentCache: make(map[string]string),
+	}
+}
+
+// AddDocuments implements selective storage based on strategy
+func (c *Client) AddDocuments(ctx context.Context, docs []sqlitevec.Document) error {
+	if len(docs) == 0 {
+		return nil
+	}
+
+	switch c.strategy {
+	case StorageAlways:
+		// Use existing implementation - store all embeddings
+		return c.base.AddDocuments(ctx, docs)
+
+	case StorageHub:
+		// Store only hub candidates
+		return c.addDocumentsSelective(ctx, docs)
+
+	case StorageOnDemand:
+		// Don't store embeddings, only cache content
+		return c.cacheDocuments(ctx, docs)
+
+	default:
+		return c.base.AddDocuments(ctx, docs)
+	}
+}
+
+// addDocumentsSelective stores embeddings only for hub-qualified documents
+func (c *Client) addDocumentsSelective(ctx context.Context, docs []sqlitevec.Document) error {
+	// Always cache content for potential recomputation
+	if err := c.cacheDocuments(ctx, docs); err != nil {
+		return err
+	}
+
+	// Filter to hub documents
+	hubDocs := make([]sqlitevec.Document, 0, len(docs))
+	for _, doc := range docs {
+		if c.isHub(doc.ID) {
+			hubDocs = append(hubDocs, doc)
+		}
+	}
+
+	// Store only hub embeddings
+	if len(hubDocs) > 0 {
+		log.Debug().
+			Int("total", len(docs)).
+			Int("hubs", len(hubDocs)).
+			Msg("Storing selective embeddings")
+		return c.base.AddDocuments(ctx, hubDocs)
+	}
+
+	log.Debug().Int("total", len(docs)).Msg("All documents cached, no hubs to store")
+	return nil
+}
+
+// cacheDocuments stores content for later recomputation
+func (c *Client) cacheDocuments(ctx context.Context, docs []sqlitevec.Document) error {
+	c.cacheMu.Lock()
+	defer c.cacheMu.Unlock()
+
+	for _, doc := range docs {
+		c.contentCache[doc.ID] = doc.Content
+	}
+
+	return nil
+}
+
+// DeleteDocuments removes documents by their IDs
+func (c *Client) DeleteDocuments(ctx context.Context, ids []string) error {
+	// Remove from base storage
+	if err := c.base.DeleteDocuments(ctx, ids); err != nil {
+		return err
+	}
+
+	// Clean up caches
+	c.mu.Lock()
+	for _, id := range ids {
+		delete(c.accessCount, id)
+		delete(c.lastAccess, id)
+	}
+	c.mu.Unlock()
+
+	c.cacheMu.Lock()
+	for _, id := range ids {
+		delete(c.contentCache, id)
+	}
+	c.cacheMu.Unlock()
+
+	return nil
+}
+
+// Query performs search with dynamic recomputation
+func (c *Client) Query(ctx context.Context, query string, limit int, where map[string]any) ([]sqlitevec.QueryResult, error) {
+	switch c.strategy {
+	case StorageAlways:
+		// Use existing implementation
+		return c.queryAndTrack(ctx, query, limit, where)
+
+	case StorageHub:
+		// Search hubs, then expand with recomputation
+		return c.queryHybrid(ctx, query, limit, where)
+
+	case StorageOnDemand:
+		// Fully dynamic search
+		return c.queryDynamic(ctx, query, limit, where)
+
+	default:
+		return c.queryAndTrack(ctx, query, limit, where)
+	}
+}
+
+// queryAndTrack wraps base Query with access tracking
+func (c *Client) queryAndTrack(ctx context.Context, query string, limit int, where map[string]any) ([]sqlitevec.QueryResult, error) {
+	results, err := c.base.Query(ctx, query, limit, where)
+	if err != nil {
+		return nil, err
+	}
+
+	// Track access for hub detection
+	c.trackAccess(results)
+
+	return results, nil
+}
+
+// queryHybrid searches stored hubs and recomputes non-hubs
+func (c *Client) queryHybrid(ctx context.Context, query string, limit int, where map[string]any) ([]sqlitevec.QueryResult, error) {
+	startTime := time.Now()
+
+	// 1. Query stored hub embeddings (limit * 2 for expansion)
+	hubResults, err := c.base.Query(ctx, query, limit*2, where)
+	if err != nil {
+		return nil, err
+	}
+
+	// 2. Track access
+	c.trackAccess(hubResults)
+
+	// 3. Get candidate non-hub IDs (from content cache)
+	candidates := c.getCandidateNonHubs(where, limit*2)
+
+	// 4. Recompute embeddings for candidates if we have any
+	var recomputedResults []sqlitevec.QueryResult
+	if len(candidates) > 0 {
+		recomputedResults, err = c.recomputeAndScore(ctx, query, candidates)
+		if err != nil {
+			// Log but don't fail - use hub results only
+			log.Warn().Err(err).Msg("Failed to recompute embeddings, using hub results only")
+			recomputedResults = nil
+		}
+	}
+
+	// 5. Merge and rank
+	allResults := append(hubResults, recomputedResults...)
+	sortBySimilarity(allResults)
+
+	// 6. Return top K
+	if len(allResults) > limit {
+		allResults = allResults[:limit]
+	}
+
+	duration := time.Since(startTime)
+	log.Debug().
+		Dur("duration_ms", duration).
+		Int("hubs", len(hubResults)).
+		Int("recomputed", len(recomputedResults)).
+		Int("results", len(allResults)).
+		Msg("Hybrid search completed")
+
+	return allResults, nil
+}
+
+// queryDynamic recomputes all embeddings on-the-fly
+func (c *Client) queryDynamic(ctx context.Context, query string, limit int, where map[string]any) ([]sqlitevec.QueryResult, error) {
+	startTime := time.Now()
+
+	// Get all candidate IDs from content cache
+	candidates := c.getCandidateNonHubs(where, limit*5)
+
+	// Recompute and score all
+	results, err := c.recomputeAndScore(ctx, query, candidates)
+	if err != nil {
+		return nil, err
+	}
+
+	// Track access
+	c.trackAccess(results)
+
+	// Return top K
+	if len(results) > limit {
+		results = results[:limit]
+	}
+
+	duration := time.Since(startTime)
+	log.Debug().
+		Dur("duration_ms", duration).
+		Int("recomputed", len(candidates)).
+		Int("results", len(results)).
+		Msg("Dynamic search completed")
+
+	return results, nil
+}
+
+// recomputeAndScore generates embeddings and computes similarities
+func (c *Client) recomputeAndScore(ctx context.Context, query string, candidateIDs []string) ([]sqlitevec.QueryResult, error) {
+	if len(candidateIDs) == 0 {
+		return nil, nil
+	}
+
+	// Generate query embedding
+	queryEmb, err := c.embedSvc.Embed(query)
+	if err != nil {
+		return nil, fmt.Errorf("embed query: %w", err)
+	}
+
+	// Get content for candidates
+	c.cacheMu.RLock()
+	texts := make([]string, 0, len(candidateIDs))
+	validIDs := make([]string, 0, len(candidateIDs))
+	for _, id := range candidateIDs {
+		if content, ok := c.contentCache[id]; ok && content != "" {
+			texts = append(texts, content)
+			validIDs = append(validIDs, id)
+		}
+	}
+	c.cacheMu.RUnlock()
+
+	if len(texts) == 0 {
+		return nil, nil
+	}
+
+	// Batch generate embeddings
+	embeddings, err := c.embedSvc.EmbedBatch(texts)
+	if err != nil {
+		return nil, fmt.Errorf("batch embed: %w", err)
+	}
+
+	// Compute similarities
+	results := make([]sqlitevec.QueryResult, len(embeddings))
+	for i, emb := range embeddings {
+		similarity := cosineSimilarity(queryEmb, emb)
+		distance := 1.0 - similarity // Convert to distance
+
+		results[i] = sqlitevec.QueryResult{
+			ID:         validIDs[i],
+			Distance:   float64(distance),
+			Similarity: float64(similarity),
+			Metadata:   make(map[string]any),
+		}
+	}
+
+	return results, nil
+}
+
+// trackAccess records document access for hub detection
+func (c *Client) trackAccess(results []sqlitevec.QueryResult) {
+	if len(results) == 0 {
+		return
+	}
+
+	c.mu.Lock()
+	defer c.mu.Unlock()
+
+	now := time.Now()
+	for _, r := range results {
+		c.accessCount[r.ID]++
+		c.lastAccess[r.ID] = now
+	}
+}
+
+// isHub checks if a document qualifies as a hub
+func (c *Client) isHub(docID string) bool {
+	c.mu.RLock()
+	defer c.mu.RUnlock()
+
+	count := c.accessCount[docID]
+	return count >= c.hubThreshold
+}
+
+// getCandidateNonHubs returns IDs of non-hub documents matching filter
+func (c *Client) getCandidateNonHubs(where map[string]any, limit int) []string {
+	c.cacheMu.RLock()
+	defer c.cacheMu.RUnlock()
+
+	candidates := make([]string, 0, limit)
+	for id := range c.contentCache {
+		if !c.isHub(id) {
+			candidates = append(candidates, id)
+			if len(candidates) >= limit {
+				break
+			}
+		}
+	}
+
+	return candidates
+}
+
+// IsConnected always returns true (wraps base client)
+func (c *Client) IsConnected() bool {
+	return c.base.IsConnected()
+}
+
+// Close releases resources
+func (c *Client) Close() error {
+	return c.base.Close()
+}
+
+// Count returns the total number of vectors in the store
+func (c *Client) Count(ctx context.Context) (int64, error) {
+	return c.base.Count(ctx)
+}
+
+// ModelVersion returns the current embedding model version
+func (c *Client) ModelVersion() string {
+	return c.base.ModelVersion()
+}
+
+// NeedsRebuild checks if vectors need to be rebuilt due to model version change
+func (c *Client) NeedsRebuild(ctx context.Context) (bool, string) {
+	return c.base.NeedsRebuild(ctx)
+}
+
+// GetStaleVectors returns doc_ids of vectors with mismatched or null model versions
+func (c *Client) GetStaleVectors(ctx context.Context) ([]sqlitevec.StaleVectorInfo, error) {
+	return c.base.GetStaleVectors(ctx)
+}
+
+// DeleteVectorsByDocIDs removes vectors by their doc_ids
+func (c *Client) DeleteVectorsByDocIDs(ctx context.Context, docIDs []string) error {
+	return c.base.DeleteVectorsByDocIDs(ctx, docIDs)
+}
+
+// GetStorageStats returns storage efficiency metrics
+func (c *Client) GetStorageStats(ctx context.Context) (StorageStats, error) {
+	c.mu.RLock()
+	c.cacheMu.RLock()
+	defer c.mu.RUnlock()
+	defer c.cacheMu.RUnlock()
+
+	totalDocs := len(c.contentCache)
+	hubCount := 0
+	for id := range c.contentCache {
+		if c.accessCount[id] >= c.hubThreshold {
+			hubCount++
+		}
+	}
+
+	storedCount := hubCount
+	if c.strategy == StorageAlways {
+		// Get actual count from database
+		if count, err := c.base.Count(ctx); err == nil {
+			storedCount = int(count)
+		}
+	} else if c.strategy == StorageOnDemand {
+		storedCount = 0
+	}
+
+	embeddingSize := 384 * 4 // 384 dims × 4 bytes (float32)
+	storedBytes := storedCount * embeddingSize
+	potentialBytes := totalDocs * embeddingSize
+
+	savingsPercent := 0.0
+	if potentialBytes > 0 {
+		savingsPercent = (1.0 - float64(storedBytes)/float64(potentialBytes)) * 100
+	}
+
+	return StorageStats{
+		TotalDocuments:   totalDocs,
+		HubDocuments:     hubCount,
+		StoredEmbeddings: storedCount,
+		StorageBytes:     storedBytes,
+		SavingsPercent:   savingsPercent,
+		Strategy:         c.strategy,
+	}, nil
+}
+
+// StorageStats contains storage efficiency metrics
+type StorageStats struct {
+	TotalDocuments   int
+	HubDocuments     int
+	StoredEmbeddings int
+	StorageBytes     int
+	SavingsPercent   float64
+	Strategy         VectorStorageStrategy
+}
+
+// Helper functions
+
+func cosineSimilarity(a, b []float32) float32 {
+	var dotProduct, normA, normB float32
+	for i := range a {
+		dotProduct += a[i] * b[i]
+		normA += a[i] * a[i]
+		normB += b[i] * b[i]
+	}
+	if normA == 0 || normB == 0 {
+		return 0
+	}
+	return dotProduct / float32(math.Sqrt(float64(normA))*math.Sqrt(float64(normB)))
+}
+
+func sortBySimilarity(results []sqlitevec.QueryResult) {
+	// Use a simple but efficient sorting algorithm
+	n := len(results)
+	for i := 0; i < n-1; i++ {
+		for j := 0; j < n-i-1; j++ {
+			if results[j].Similarity < results[j+1].Similarity {
+				results[j], results[j+1] = results[j+1], results[j]
+			}
+		}
+	}
+}
+
+func strategyToString(s VectorStorageStrategy) string {
+	switch s {
+	case StorageAlways:
+		return "always"
+	case StorageHub:
+		return "hub"
+	case StorageOnDemand:
+		return "on_demand"
+	default:
+		return "unknown"
+	}
+}
+
+// ParseStrategy converts a string to VectorStorageStrategy
+func ParseStrategy(s string) VectorStorageStrategy {
+	switch s {
+	case "hub":
+		return StorageHub
+	case "on_demand":
+		return StorageOnDemand
+	case "always":
+		return StorageAlways
+	default:
+		return StorageHub // Default to hub strategy
+	}
+}

internal/vector/hybrid/client_test.go

@@ -0,0 +1,186 @@
+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"])
+}

internal/vector/hybrid/config.go

@@ -0,0 +1,62 @@
+package hybrid
+
+import (
+	"os"
+	"strconv"
+
+	"github.com/rs/zerolog/log"
+)
+
+// GetStrategyFromEnv reads CLAUDE_MNEMONIC_VECTOR_STRATEGY from environment
+func GetStrategyFromEnv() VectorStorageStrategy {
+	strategyStr := os.Getenv("CLAUDE_MNEMONIC_VECTOR_STRATEGY")
+	if strategyStr == "" {
+		// Default to hub strategy for optimal balance
+		return StorageHub
+	}
+
+	strategy := ParseStrategy(strategyStr)
+	log.Info().
+		Str("env_value", strategyStr).
+		Str("strategy", strategyToString(strategy)).
+		Msg("Vector storage strategy from environment")
+
+	return strategy
+}
+
+// GetHubThresholdFromEnv reads CLAUDE_MNEMONIC_HUB_THRESHOLD from environment
+func GetHubThresholdFromEnv() int {
+	thresholdStr := os.Getenv("CLAUDE_MNEMONIC_HUB_THRESHOLD")
+	if thresholdStr == "" {
+		return 5 // Default threshold
+	}
+
+	threshold, err := strconv.Atoi(thresholdStr)
+	if err != nil {
+		log.Warn().
+			Err(err).
+			Str("env_value", thresholdStr).
+			Msg("Invalid hub threshold in environment, using default")
+		return 5
+	}
+
+	if threshold < 1 {
+		log.Warn().
+			Int("env_value", threshold).
+			Msg("Hub threshold too low, using minimum of 1")
+		return 1
+	}
+
+	log.Info().
+		Int("threshold", threshold).
+		Msg("Hub threshold from environment")
+
+	return threshold
+}
+
+// IsHybridEnabled checks if hybrid storage should be used
+// Returns false if CLAUDE_MNEMONIC_VECTOR_STRATEGY=always (backwards compat)
+func IsHybridEnabled() bool {
+	strategy := GetStrategyFromEnv()
+	return strategy != StorageAlways
+}

internal/vector/hybrid/graph_search.go

@@ -0,0 +1,308 @@
+package hybrid
+
+import (
+	"context"
+	"fmt"
+	"sort"
+	"time"
+
+	"github.com/lukaszraczylo/claude-mnemonic/internal/graph"
+	"github.com/lukaszraczylo/claude-mnemonic/internal/vector/sqlitevec"
+	"github.com/lukaszraczylo/claude-mnemonic/pkg/models"
+	"github.com/rs/zerolog/log"
+)
+
+// GraphConfig configures graph-aware search
+type GraphConfig struct {
+	Enabled      bool
+	MaxHops      int     // Maximum graph traversal depth (default: 2)
+	BranchFactor int     // Number of neighbors to expand per node (default: 5)
+	EdgeWeight   float64 // Minimum edge weight to follow (default: 0.3)
+}
+
+// DefaultGraphConfig returns sensible defaults for graph search
+func DefaultGraphConfig() GraphConfig {
+	return GraphConfig{
+		Enabled:      true,
+		MaxHops:      2,
+		BranchFactor: 5,
+		EdgeWeight:   0.3,
+	}
+}
+
+// GraphSearchClient wraps hybrid.Client with graph-aware search
+type GraphSearchClient struct {
+	*Client
+	graph       *graph.ObservationGraph
+	graphConfig GraphConfig
+}
+
+// NewGraphSearchClient creates a graph-enhanced hybrid client
+func NewGraphSearchClient(baseClient *Client, observationGraph *graph.ObservationGraph, cfg GraphConfig) *GraphSearchClient {
+	return &GraphSearchClient{
+		Client:      baseClient,
+		graph:       observationGraph,
+		graphConfig: cfg,
+	}
+}
+
+// Query performs graph-aware vector search with two-level traversal
+func (g *GraphSearchClient) Query(ctx context.Context, query string, limit int, where map[string]any) ([]sqlitevec.QueryResult, error) {
+	if !g.graphConfig.Enabled || g.graph == nil {
+		// Fall back to standard hybrid search
+		return g.Client.Query(ctx, query, limit, where)
+	}
+
+	startTime := time.Now()
+
+	// 1. Generate query embedding
+	queryEmb, err := g.embedSvc.Embed(query)
+	if err != nil {
+		return nil, fmt.Errorf("embed query: %w", err)
+	}
+
+	// 2. Search hub nodes (stored embeddings)
+	hubResults, err := g.base.Query(ctx, query, limit*2, where)
+	if err != nil {
+		// Fall back to standard search on error
+		log.Warn().Err(err).Msg("Hub search failed, falling back to hybrid search")
+		return g.Client.Query(ctx, query, limit, where)
+	}
+
+	// 3. Track hub access
+	g.trackAccess(hubResults)
+
+	// 4. Expand via graph traversal
+	expandedIDs := g.expandFromHubs(hubResults, limit*4)
+
+	// 5. Filter to non-hubs that need recomputation
+	nonHubIDs := make([]string, 0)
+	for _, id := range expandedIDs {
+		if !g.isHub(id) {
+			nonHubIDs = append(nonHubIDs, id)
+		}
+	}
+
+	// 6. Batch recompute non-hub embeddings
+	recomputedResults, err := g.recomputeAndScore(ctx, query, nonHubIDs)
+	if err != nil {
+		log.Warn().Err(err).Msg("Recomputation failed, using hub results only")
+		recomputedResults = nil
+	}
+
+	// 7. Apply graph-based ranking boost
+	allResults := g.mergeAndRankWithGraph(hubResults, recomputedResults, queryEmb)
+
+	// 8. Return top K
+	if len(allResults) > limit {
+		allResults = allResults[:limit]
+	}
+
+	duration := time.Since(startTime)
+	log.Debug().
+		Dur("duration_ms", duration).
+		Int("hubs", len(hubResults)).
+		Int("expanded", len(expandedIDs)).
+		Int("recomputed", len(recomputedResults)).
+		Int("results", len(allResults)).
+		Msg("Graph search completed")
+
+	return allResults, nil
+}
+
+// expandFromHubs traverses graph from hub nodes to find promising candidates
+func (g *GraphSearchClient) expandFromHubs(hubResults []sqlitevec.QueryResult, maxCandidates int) []string {
+	if g.graph == nil {
+		return nil
+	}
+
+	expanded := make(map[string]float64) // doc_id -> relevance score
+	visited := make(map[int64]bool)
+
+	// Start from top hub results
+	for i, result := range hubResults {
+		if i >= g.graphConfig.BranchFactor*2 {
+			break // Limit starting points
+		}
+
+		// Parse observation ID from doc_id
+		obsID := parseObservationID(result.ID)
+		if obsID == 0 {
+			continue
+		}
+
+		// Mark as visited with high relevance (direct match)
+		visited[obsID] = true
+		expanded[result.ID] = result.Similarity
+
+		// Traverse graph from this hub
+		g.traverseGraph(obsID, result.Similarity, 0, expanded, visited)
+	}
+
+	// Convert to sorted list
+	type candidate struct {
+		ID        string
+		Relevance float64
+	}
+
+	candidates := make([]candidate, 0, len(expanded))
+	for id, rel := range expanded {
+		candidates = append(candidates, candidate{ID: id, Relevance: rel})
+	}
+
+	// Sort by relevance descending
+	sort.Slice(candidates, func(i, j int) bool {
+		return candidates[i].Relevance > candidates[j].Relevance
+	})
+
+	// Return top candidates
+	if len(candidates) > maxCandidates {
+		candidates = candidates[:maxCandidates]
+	}
+
+	result := make([]string, len(candidates))
+	for i, c := range candidates {
+		result[i] = c.ID
+	}
+
+	return result
+}
+
+// traverseGraph performs depth-limited graph traversal
+func (g *GraphSearchClient) traverseGraph(nodeID int64, baseRelevance float64, depth int, expanded map[string]float64, visited map[int64]bool) {
+	if depth >= g.graphConfig.MaxHops {
+		return // Max depth reached
+	}
+
+	// Get neighbors from graph
+	neighbors, weights, err := g.graph.GetNeighbors(nodeID)
+	if err != nil {
+		return // No neighbors or error
+	}
+
+	// Traverse top neighbors by weight
+	type neighborWeight struct {
+		ID     int64
+		Weight float32
+	}
+
+	neighborList := make([]neighborWeight, len(neighbors))
+	for i := range neighbors {
+		neighborList[i] = neighborWeight{
+			ID:     neighbors[i],
+			Weight: weights[i],
+		}
+	}
+
+	// Sort by weight descending
+	sort.Slice(neighborList, func(i, j int) bool {
+		return neighborList[i].Weight > neighborList[j].Weight
+	})
+
+	// Expand top branch_factor neighbors
+	expanded_count := 0
+	for _, nw := range neighborList {
+		if expanded_count >= g.graphConfig.BranchFactor {
+			break
+		}
+
+		// Skip if edge weight too low
+		if float64(nw.Weight) < g.graphConfig.EdgeWeight {
+			continue
+		}
+
+		// Skip if already visited
+		if visited[nw.ID] {
+			continue
+		}
+		visited[nw.ID] = true
+
+		// Calculate propagated relevance (decays with distance)
+		decay := 0.7 // 30% decay per hop
+		propagatedRelevance := baseRelevance * float64(nw.Weight) * decay
+
+		// Add to expanded set
+		docID := formatObservationDocID(nw.ID)
+		if existing, ok := expanded[docID]; !ok || propagatedRelevance > existing {
+			expanded[docID] = propagatedRelevance
+		}
+
+		// Recursively traverse
+		g.traverseGraph(nw.ID, propagatedRelevance, depth+1, expanded, visited)
+		expanded_count++
+	}
+}
+
+// mergeAndRankWithGraph combines hub and recomputed results with graph-based ranking
+func (g *GraphSearchClient) mergeAndRankWithGraph(hubResults, recomputedResults []sqlitevec.QueryResult, queryEmb []float32) []sqlitevec.QueryResult {
+	// Merge results
+	allResults := append(hubResults, recomputedResults...)
+
+	// Apply graph-based re-ranking
+	if g.graph != nil {
+		for i := range allResults {
+			obsID := parseObservationID(allResults[i].ID)
+			if obsID == 0 {
+				continue
+			}
+
+			// Boost score based on node degree (hubs are more important)
+			node, err := g.graph.GetNode(obsID)
+			if err == nil && node.Degree > 0 {
+				// Degree boost: up to 10% increase for high-degree nodes
+				degreeBoost := 1.0 + (0.1 * float64(node.Degree) / 20.0)
+				if degreeBoost > 1.1 {
+					degreeBoost = 1.1
+				}
+				allResults[i].Similarity *= degreeBoost
+			}
+		}
+	}
+
+	// Sort by adjusted similarity
+	sortBySimilarity(allResults)
+
+	return allResults
+}
+
+// parseObservationID extracts observation ID from doc_id
+// Format: "obs-{id}-{field}"
+func parseObservationID(docID string) int64 {
+	var obsID int64
+	// Ignore error - returns 0 on parse failure, which callers handle
+	_, _ = fmt.Sscanf(docID, "obs-%d-", &obsID)
+	return obsID
+}
+
+// formatObservationDocID creates a doc_id for an observation
+func formatObservationDocID(obsID int64) string {
+	return fmt.Sprintf("obs-%d-combined", obsID)
+}
+
+// GetGraphStats returns statistics about the observation graph
+func (g *GraphSearchClient) GetGraphStats() graph.GraphStats {
+	if g.graph == nil {
+		return graph.GraphStats{}
+	}
+	return g.graph.Stats()
+}
+
+// RebuildGraph rebuilds the observation graph from current observations
+// This should be called periodically or when observations change significantly
+func (g *GraphSearchClient) RebuildGraph(ctx context.Context, observations []*models.Observation) error {
+	log.Info().Int("observations", len(observations)).Msg("Rebuilding observation graph")
+
+	newGraph, err := graph.BuildFromObservations(ctx, observations)
+	if err != nil {
+		return fmt.Errorf("build graph: %w", err)
+	}
+
+	g.graph = newGraph
+
+	log.Info().
+		Int("nodes", newGraph.Stats().NodeCount).
+		Int("edges", newGraph.Stats().EdgeCount).
+		Msg("Graph rebuilt successfully")
+
+	return nil
+}

internal/vector/hybrid/interface_test.go

@@ -0,0 +1,16 @@
+package hybrid
+
+import (
+	"testing"
+
+	"github.com/lukaszraczylo/claude-mnemonic/internal/vector"
+)
+
+// TestInterfaceImplementation verifies that hybrid clients implement vector.Client interface
+func TestInterfaceImplementation(t *testing.T) {
+	// Compile-time check that Client implements vector.Client
+	var _ vector.Client = (*Client)(nil)
+
+	// Compile-time check that GraphSearchClient implements vector.Client
+	var _ vector.Client = (*GraphSearchClient)(nil)
+}

internal/vector/hybrid/metrics.go

@@ -0,0 +1,272 @@
+package hybrid
+
+import (
+	"fmt"
+	"sync"
+	"sync/atomic"
+	"time"
+)
+
+// Metrics tracks performance and usage statistics for hybrid vector storage
+type Metrics struct {
+	startTime         time.Time
+	recentLatencies   []time.Duration
+	latenciesMu       sync.Mutex
+	totalQueries      atomic.Int64
+	hubOnlyQueries    atomic.Int64
+	hybridQueries     atomic.Int64
+	onDemandQueries   atomic.Int64
+	graphQueries      atomic.Int64
+	totalLatency      atomic.Int64 // Sum in microseconds
+	hubLatency        atomic.Int64
+	recomputeLatency  atomic.Int64
+	totalDocuments    atomic.Int64
+	hubDocuments      atomic.Int64
+	storedEmbeddings  atomic.Int64
+	recomputedCount   atomic.Int64
+	cacheHits         atomic.Int64
+	cacheMisses       atomic.Int64
+	graphTraversals   atomic.Int64
+	avgTraversalDepth atomic.Int64
+}
+
+// NewMetrics creates a new metrics tracker
+func NewMetrics() *Metrics {
+	return &Metrics{
+		recentLatencies: make([]time.Duration, 0, 1000),
+		startTime:       time.Now(),
+	}
+}
+
+// RecordQuery records a query execution
+func (m *Metrics) RecordQuery(queryType string, latency time.Duration, recomputed int) {
+	m.totalQueries.Add(1)
+	m.totalLatency.Add(latency.Microseconds())
+
+	switch queryType {
+	case "hub_only":
+		m.hubOnlyQueries.Add(1)
+	case "hybrid":
+		m.hybridQueries.Add(1)
+	case "on_demand":
+		m.onDemandQueries.Add(1)
+	case "graph":
+		m.graphQueries.Add(1)
+	}
+
+	if recomputed > 0 {
+		m.recomputedCount.Add(int64(recomputed))
+	}
+
+	// Track recent latencies
+	m.latenciesMu.Lock()
+	m.recentLatencies = append(m.recentLatencies, latency)
+	if len(m.recentLatencies) > 1000 {
+		m.recentLatencies = m.recentLatencies[len(m.recentLatencies)-1000:]
+	}
+	m.latenciesMu.Unlock()
+}
+
+// RecordHubLatency records time spent in hub search
+func (m *Metrics) RecordHubLatency(latency time.Duration) {
+	m.hubLatency.Add(latency.Microseconds())
+}
+
+// RecordRecomputeLatency records time spent recomputing embeddings
+func (m *Metrics) RecordRecomputeLatency(latency time.Duration) {
+	m.recomputeLatency.Add(latency.Microseconds())
+}
+
+// RecordCacheHit records a content cache hit
+func (m *Metrics) RecordCacheHit() {
+	m.cacheHits.Add(1)
+}
+
+// RecordCacheMiss records a content cache miss
+func (m *Metrics) RecordCacheMiss() {
+	m.cacheMisses.Add(1)
+}
+
+// RecordGraphTraversal records a graph traversal operation
+func (m *Metrics) RecordGraphTraversal(depth int) {
+	m.graphTraversals.Add(1)
+	m.avgTraversalDepth.Add(int64(depth))
+}
+
+// UpdateStorageStats updates current storage statistics
+func (m *Metrics) UpdateStorageStats(total, hubs, stored int) {
+	m.totalDocuments.Store(int64(total))
+	m.hubDocuments.Store(int64(hubs))
+	m.storedEmbeddings.Store(int64(stored))
+}
+
+// GetSnapshot returns current metrics snapshot
+func (m *Metrics) GetSnapshot() MetricsSnapshot {
+	m.latenciesMu.Lock()
+	defer m.latenciesMu.Unlock()
+
+	totalQueries := m.totalQueries.Load()
+
+	snapshot := MetricsSnapshot{
+		// Query counts
+		TotalQueries:    totalQueries,
+		HubOnlyQueries:  m.hubOnlyQueries.Load(),
+		HybridQueries:   m.hybridQueries.Load(),
+		OnDemandQueries: m.onDemandQueries.Load(),
+		GraphQueries:    m.graphQueries.Load(),
+
+		// Storage
+		TotalDocuments:   int(m.totalDocuments.Load()),
+		HubDocuments:     int(m.hubDocuments.Load()),
+		StoredEmbeddings: int(m.storedEmbeddings.Load()),
+		RecomputedTotal:  m.recomputedCount.Load(),
+
+		// Cache
+		CacheHits:   m.cacheHits.Load(),
+		CacheMisses: m.cacheMisses.Load(),
+
+		// Graph
+		GraphTraversals: m.graphTraversals.Load(),
+
+		// Runtime
+		Uptime: time.Since(m.startTime),
+	}
+
+	// Calculate latencies
+	if totalQueries > 0 {
+		snapshot.AvgLatency = time.Duration(m.totalLatency.Load()/totalQueries) * time.Microsecond
+		snapshot.AvgHubLatency = time.Duration(m.hubLatency.Load()/totalQueries) * time.Microsecond
+	}
+
+	if m.recomputedCount.Load() > 0 {
+		snapshot.AvgRecomputeLatency = time.Duration(m.recomputeLatency.Load()/m.recomputedCount.Load()) * time.Microsecond
+	}
+
+	// Calculate percentiles
+	if len(m.recentLatencies) > 0 {
+		sorted := make([]time.Duration, len(m.recentLatencies))
+		copy(sorted, m.recentLatencies)
+		sortDurations(sorted)
+
+		snapshot.P50Latency = percentile(sorted, 0.50)
+		snapshot.P95Latency = percentile(sorted, 0.95)
+		snapshot.P99Latency = percentile(sorted, 0.99)
+	}
+
+	// Calculate cache hit rate
+	totalCacheOps := snapshot.CacheHits + snapshot.CacheMisses
+	if totalCacheOps > 0 {
+		snapshot.CacheHitRate = float64(snapshot.CacheHits) / float64(totalCacheOps)
+	}
+
+	// Calculate storage savings
+	if snapshot.TotalDocuments > 0 {
+		embeddingSize := 384 * 4 // 384 dims × 4 bytes
+		fullStorage := snapshot.TotalDocuments * embeddingSize
+		actualStorage := snapshot.StoredEmbeddings * embeddingSize
+
+		if fullStorage > 0 {
+			snapshot.StorageSavingsPercent = (1.0 - float64(actualStorage)/float64(fullStorage)) * 100
+		}
+	}
+
+	// Calculate avg traversal depth
+	if snapshot.GraphTraversals > 0 {
+		snapshot.AvgTraversalDepth = float64(m.avgTraversalDepth.Load()) / float64(snapshot.GraphTraversals)
+	}
+
+	return snapshot
+}
+
+// MetricsSnapshot represents a point-in-time metrics snapshot
+type MetricsSnapshot struct {
+	// Query metrics
+	TotalQueries    int64
+	HubOnlyQueries  int64
+	HybridQueries   int64
+	OnDemandQueries int64
+	GraphQueries    int64
+
+	// Latency metrics
+	AvgLatency          time.Duration
+	P50Latency          time.Duration
+	P95Latency          time.Duration
+	P99Latency          time.Duration
+	AvgHubLatency       time.Duration
+	AvgRecomputeLatency time.Duration
+
+	// Storage metrics
+	TotalDocuments        int
+	HubDocuments          int
+	StoredEmbeddings      int
+	StorageSavingsPercent float64
+	RecomputedTotal       int64
+
+	// Cache metrics
+	CacheHits    int64
+	CacheMisses  int64
+	CacheHitRate float64
+
+	// Graph metrics
+	GraphTraversals   int64
+	AvgTraversalDepth float64
+
+	// Runtime
+	Uptime time.Duration
+}
+
+// sortDurations sorts a slice of durations in ascending order
+func sortDurations(durations []time.Duration) {
+	n := len(durations)
+	for i := 0; i < n-1; i++ {
+		for j := 0; j < n-i-1; j++ {
+			if durations[j] > durations[j+1] {
+				durations[j], durations[j+1] = durations[j+1], durations[j]
+			}
+		}
+	}
+}
+
+// percentile calculates the Nth percentile from a sorted slice
+func percentile(sorted []time.Duration, p float64) time.Duration {
+	if len(sorted) == 0 {
+		return 0
+	}
+
+	idx := int(float64(len(sorted)) * p)
+	if idx >= len(sorted) {
+		idx = len(sorted) - 1
+	}
+
+	return sorted[idx]
+}
+
+// String returns a human-readable representation of metrics
+func (s MetricsSnapshot) String() string {
+	return fmt.Sprintf(`Hybrid Vector Storage Metrics:
+  Queries:
+    Total: %d (Hub: %d, Hybrid: %d, OnDemand: %d, Graph: %d)
+    Avg Latency: %v (p50: %v, p95: %v, p99: %v)
+    Hub Latency: %v, Recompute Latency: %v
+  Storage:
+    Documents: %d (Hubs: %d, %.1f%%)
+    Stored Embeddings: %d
+    Savings: %.1f%%
+    Total Recomputed: %d
+  Cache:
+    Hits: %d, Misses: %d (Hit Rate: %.1f%%)
+  Graph:
+    Traversals: %d (Avg Depth: %.2f)
+  Runtime: %v`,
+		s.TotalQueries, s.HubOnlyQueries, s.HybridQueries, s.OnDemandQueries, s.GraphQueries,
+		s.AvgLatency, s.P50Latency, s.P95Latency, s.P99Latency,
+		s.AvgHubLatency, s.AvgRecomputeLatency,
+		s.TotalDocuments, s.HubDocuments, float64(s.HubDocuments)/float64(s.TotalDocuments)*100,
+		s.StoredEmbeddings,
+		s.StorageSavingsPercent,
+		s.RecomputedTotal,
+		s.CacheHits, s.CacheMisses, s.CacheHitRate*100,
+		s.GraphTraversals, s.AvgTraversalDepth,
+		s.Uptime,
+	)
+}

internal/vector/interface.go

@@ -0,0 +1,42 @@
+// Package vector provides common interfaces for vector storage implementations
+package vector
+
+import (
+	"context"
+
+	"github.com/lukaszraczylo/claude-mnemonic/internal/vector/sqlitevec"
+)
+
+// Client defines the interface for vector storage operations.
+// Both sqlitevec.Client and hybrid.Client implement this interface.
+type Client interface {
+	// AddDocuments adds documents with their embeddings to the vector store
+	AddDocuments(ctx context.Context, docs []sqlitevec.Document) error
+
+	// DeleteDocuments removes documents by their IDs
+	DeleteDocuments(ctx context.Context, ids []string) error
+
+	// Query performs a vector similarity search
+	Query(ctx context.Context, query string, limit int, where map[string]any) ([]sqlitevec.QueryResult, error)
+
+	// IsConnected checks if the vector store is available
+	IsConnected() bool
+
+	// Close releases resources
+	Close() error
+
+	// Count returns the total number of vectors in the store
+	Count(ctx context.Context) (int64, error)
+
+	// ModelVersion returns the current embedding model version
+	ModelVersion() string
+
+	// NeedsRebuild checks if vectors need to be rebuilt due to model version change
+	NeedsRebuild(ctx context.Context) (bool, string)
+
+	// GetStaleVectors returns doc_ids of vectors with mismatched or null model versions
+	GetStaleVectors(ctx context.Context) ([]sqlitevec.StaleVectorInfo, error)
+
+	// DeleteVectorsByDocIDs removes vectors by their doc_ids
+	DeleteVectorsByDocIDs(ctx context.Context, docIDs []string) error
+}

internal/worker/handlers.go

@@ -1312,3 +1312,85 @@ func (s *Service) handleRestart(w http.ResponseWriter, r *http.Request) {
 		}
 	}()
 }
+
+// handleGetGraphStats returns observation graph statistics.
+func (s *Service) handleGetGraphStats(w http.ResponseWriter, r *http.Request) {
+	if s.graphSearchClient == nil {
+		writeJSON(w, map[string]interface{}{
+			"enabled": false,
+			"message": "Graph search not enabled",
+		})
+		return
+	}
+
+	stats := s.graphSearchClient.GetGraphStats()
+
+	response := map[string]interface{}{
+		"enabled":      s.config.GraphEnabled,
+		"nodeCount":    stats.NodeCount,
+		"edgeCount":    stats.EdgeCount,
+		"avgDegree":    stats.AvgDegree,
+		"maxDegree":    stats.MaxDegree,
+		"minDegree":    stats.MinDegree,
+		"medianDegree": stats.MedianDegree,
+		"edgeTypes":    stats.EdgeTypes,
+		"config": map[string]interface{}{
+			"maxHops":            s.config.GraphMaxHops,
+			"branchFactor":       s.config.GraphBranchFactor,
+			"edgeWeight":         s.config.GraphEdgeWeight,
+			"rebuildIntervalMin": s.config.GraphRebuildIntervalMin,
+		},
+	}
+
+	writeJSON(w, response)
+}
+
+// handleGetVectorMetrics returns hybrid vector storage metrics.
+func (s *Service) handleGetVectorMetrics(w http.ResponseWriter, r *http.Request) {
+	if s.hybridMetrics == nil {
+		writeJSON(w, map[string]interface{}{
+			"enabled": false,
+			"message": "Vector metrics not available",
+		})
+		return
+	}
+
+	snapshot := s.hybridMetrics.GetSnapshot()
+
+	response := map[string]interface{}{
+		"queries": map[string]interface{}{
+			"total":    snapshot.TotalQueries,
+			"hubOnly":  snapshot.HubOnlyQueries,
+			"hybrid":   snapshot.HybridQueries,
+			"onDemand": snapshot.OnDemandQueries,
+			"graph":    snapshot.GraphQueries,
+		},
+		"latency": map[string]interface{}{
+			"avg":          snapshot.AvgLatency.String(),
+			"p50":          snapshot.P50Latency.String(),
+			"p95":          snapshot.P95Latency.String(),
+			"p99":          snapshot.P99Latency.String(),
+			"avgHub":       snapshot.AvgHubLatency.String(),
+			"avgRecompute": snapshot.AvgRecomputeLatency.String(),
+		},
+		"storage": map[string]interface{}{
+			"totalDocuments":   snapshot.TotalDocuments,
+			"hubDocuments":     snapshot.HubDocuments,
+			"storedEmbeddings": snapshot.StoredEmbeddings,
+			"savingsPercent":   snapshot.StorageSavingsPercent,
+			"recomputedTotal":  snapshot.RecomputedTotal,
+		},
+		"cache": map[string]interface{}{
+			"hits":    snapshot.CacheHits,
+			"misses":  snapshot.CacheMisses,
+			"hitRate": snapshot.CacheHitRate,
+		},
+		"graph": map[string]interface{}{
+			"traversals": snapshot.GraphTraversals,
+			"avgDepth":   snapshot.AvgTraversalDepth,
+		},
+		"uptime": snapshot.Uptime.String(),
+	}
+
+	writeJSON(w, response)
+}

internal/worker/service.go

@@ -24,6 +24,8 @@ import (
 	"github.com/lukaszraczylo/claude-mnemonic/internal/scoring"
 	"github.com/lukaszraczylo/claude-mnemonic/internal/search/expansion"
 	"github.com/lukaszraczylo/claude-mnemonic/internal/update"
+	"github.com/lukaszraczylo/claude-mnemonic/internal/vector"
+	"github.com/lukaszraczylo/claude-mnemonic/internal/vector/hybrid"
 	"github.com/lukaszraczylo/claude-mnemonic/internal/vector/sqlitevec"
 	"github.com/lukaszraczylo/claude-mnemonic/internal/watcher"
 	"github.com/lukaszraczylo/claude-mnemonic/internal/worker/sdk"
@@ -62,7 +64,15 @@ type RetrievalStats struct {
 type Service struct {
 	startTime          time.Time
 	initError          error
+	vectorClient       vector.Client
 	ctx                context.Context
+	sseBroadcaster     *sse.Broadcaster
+	server             *http.Server
+	graphRebuildTicker *time.Ticker
+	hybridMetrics      *hybrid.Metrics
+	graphSearchClient  *hybrid.GraphSearchClient
+	retrievalStats     map[string]*RetrievalStats
+	staleQueue         chan staleVerifyRequest
 	queryExpander      *expansion.Expander
 	recalculator       *scoring.Recalculator
 	summaryStore       *sqlite.SummaryStore
@@ -72,25 +82,20 @@ type Service struct {
 	relationStore      *sqlite.RelationStore
 	patternDetector    *pattern.Detector
 	sessionManager     *session.Manager
-	sseBroadcaster   *sse.Broadcaster
 	router             *chi.Mux
-	embedSvc         *embedding.Service
-	vectorClient     *sqlitevec.Client
+	config             *config.Config
+	store              *sqlite.Store
 	vectorSync         *sqlitevec.Sync
 	reranker           *reranking.Service
 	updater            *update.Updater
 	observationStore   *sqlite.ObservationStore
 	scoreCalculator    *scoring.Calculator
 	processor          *sdk.Processor
-	server           *http.Server
-	sessionStore     *sqlite.SessionStore
-	retrievalStats   map[string]*RetrievalStats
-	configWatcher    *watcher.Watcher
-	store            *sqlite.Store
-	cancel           context.CancelFunc
 	dbWatcher          *watcher.Watcher
-	staleQueue       chan staleVerifyRequest
-	config           *config.Config
+	sessionStore       *sqlite.SessionStore
+	configWatcher      *watcher.Watcher
+	embedSvc           *embedding.Service
+	cancel             context.CancelFunc
 	version            string
 	wg                 sync.WaitGroup
 	initMu             sync.RWMutex
@@ -185,7 +190,7 @@ func (s *Service) initializeAsync() {
 
 	// Create embedding service and sqlite-vec client for vector search (optional)
 	var embedSvc *embedding.Service
-	var vectorClient *sqlitevec.Client
+	var vectorClient vector.Client
 	var vectorSync *sqlitevec.Sync
 
 	var reranker *reranking.Service
@@ -196,14 +201,35 @@ func (s *Service) initializeAsync() {
 	} else {
 		embedSvc = emb
 		// Create sqlite-vec client using the same DB connection
-		client, clientErr := sqlitevec.NewClient(sqlitevec.Config{
+		baseClient, clientErr := sqlitevec.NewClient(sqlitevec.Config{
 			DB: store.DB(),
 		}, embedSvc)
 		if clientErr != nil {
 			log.Warn().Err(clientErr).Msg("sqlite-vec client creation failed - vector search disabled")
 		} else {
-			vectorClient = client
-			vectorSync = sqlitevec.NewSync(client)
+			// Wrap with LEANN hybrid storage client
+			strategy := hybrid.GetStrategyFromEnv()
+			hybridClient := hybrid.NewClient(hybrid.Config{
+				BaseClient:   baseClient,
+				DB:           store.DB(),
+				EmbedSvc:     embedSvc,
+				Strategy:     strategy,
+				HubThreshold: hybrid.GetHubThresholdFromEnv(),
+			})
+
+			// Wrap with graph-aware search client
+			graphConfig := hybrid.GraphConfig{
+				Enabled:      s.config.GraphEnabled,
+				MaxHops:      s.config.GraphMaxHops,
+				BranchFactor: s.config.GraphBranchFactor,
+				EdgeWeight:   s.config.GraphEdgeWeight,
+			}
+			graphClient := hybrid.NewGraphSearchClient(hybridClient, nil, graphConfig)
+			vectorClient = graphClient
+			s.graphSearchClient = graphClient
+			s.hybridMetrics = hybrid.NewMetrics()
+
+			vectorSync = sqlitevec.NewSync(baseClient)
 
 			// Initialize AST-aware code chunking
 			chunkOpts := chunking.DefaultChunkOptions()
@@ -215,10 +241,28 @@ func (s *Service) initializeAsync() {
 			chunkingManager := chunking.NewManager(chunkers, chunkOpts)
 			vectorSync.SetChunkingManager(chunkingManager)
 
+			strategyName := "hub" // default
+			switch strategy {
+			case hybrid.StorageAlways:
+				strategyName = "always"
+			case hybrid.StorageOnDemand:
+				strategyName = "on_demand"
+			}
+
 			log.Info().
 				Str("model", embedSvc.Version()).
+				Str("vector_strategy", strategyName).
+				Bool("graph_enabled", s.config.GraphEnabled).
 				Strs("chunkers", []string{"go", "python", "typescript"}).
 				Msg("sqlite-vec vector search with AST-aware code chunking enabled")
+
+			if s.config.GraphEnabled {
+				log.Info().
+					Int("max_hops", s.config.GraphMaxHops).
+					Int("branch_factor", s.config.GraphBranchFactor).
+					Float64("edge_weight", s.config.GraphEdgeWeight).
+					Msg("Graph-aware search configured (graph will be built after initialization)")
+			}
 		}
 
 		// Create cross-encoder reranking service if enabled
@@ -409,6 +453,12 @@ func (s *Service) initializeAsync() {
 	// Start file watchers for auto-recreation on deletion
 	s.startWatchers()
 
+	// Build initial observation graph in background if graph search is enabled
+	if s.config.GraphEnabled && s.graphSearchClient != nil {
+		s.wg.Add(1)
+		go s.buildInitialGraph(observationStore)
+	}
+
 	// Check if vectors need rebuilding (empty or model version mismatch) and trigger background rebuild
 	if vectorClient != nil && vectorSync != nil {
 		needsRebuild, reason := vectorClient.NeedsRebuild(s.ctx)
@@ -876,7 +926,7 @@ func (s *Service) rebuildStaleVectors(
 	observationStore *sqlite.ObservationStore,
 	summaryStore *sqlite.SummaryStore,
 	promptStore *sqlite.PromptStore,
-	vectorClient *sqlitevec.Client,
+	vectorClient vector.Client,
 	vectorSync *sqlitevec.Sync,
 ) {
 	defer s.wg.Done()
@@ -1041,6 +1091,113 @@ func (s *Service) verifyStaleObservation(req staleVerifyRequest) {
 	}
 }
 
+// buildInitialGraph builds the observation graph from all observations in background.
+func (s *Service) buildInitialGraph(observationStore *sqlite.ObservationStore) {
+	defer s.wg.Done()
+
+	log.Info().Msg("Building initial observation graph...")
+	start := time.Now()
+
+	// Fetch all observations
+	observations, err := observationStore.GetAllObservations(s.ctx)
+	if err != nil {
+		log.Error().Err(err).Msg("Failed to fetch observations for graph building")
+		return
+	}
+
+	if len(observations) == 0 {
+		log.Info().Msg("No observations to build graph from")
+		return
+	}
+
+	// Build graph using RebuildGraph method
+	if err := s.graphSearchClient.RebuildGraph(s.ctx, observations); err != nil {
+		log.Error().Err(err).Msg("Failed to build observation graph")
+		return
+	}
+
+	elapsed := time.Since(start)
+	stats := s.graphSearchClient.GetGraphStats()
+
+	log.Info().
+		Int("observations", len(observations)).
+		Int("nodes", stats.NodeCount).
+		Int("edges", stats.EdgeCount).
+		Float64("avg_degree", stats.AvgDegree).
+		Int("max_degree", stats.MaxDegree).
+		Dur("elapsed", elapsed).
+		Msg("Initial observation graph built successfully")
+
+	// Start periodic graph rebuild if configured
+	if s.config.GraphRebuildIntervalMin > 0 {
+		s.startGraphRebuildTimer(observationStore)
+	}
+}
+
+// startGraphRebuildTimer starts a periodic ticker to rebuild the observation graph.
+func (s *Service) startGraphRebuildTimer(observationStore *sqlite.ObservationStore) {
+	interval := time.Duration(s.config.GraphRebuildIntervalMin) * time.Minute
+	s.graphRebuildTicker = time.NewTicker(interval)
+
+	log.Info().
+		Dur("interval", interval).
+		Msg("Started periodic graph rebuild timer")
+
+	s.wg.Add(1)
+	go func() {
+		defer s.wg.Done()
+		defer s.graphRebuildTicker.Stop()
+
+		for {
+			select {
+			case <-s.ctx.Done():
+				return
+			case <-s.graphRebuildTicker.C:
+				log.Info().Msg("Periodic graph rebuild triggered")
+				s.rebuildGraph(observationStore)
+			}
+		}
+	}()
+}
+
+// rebuildGraph rebuilds the observation graph from current observations.
+func (s *Service) rebuildGraph(observationStore *sqlite.ObservationStore) {
+	if s.graphSearchClient == nil {
+		return
+	}
+
+	start := time.Now()
+
+	// Fetch all observations
+	observations, err := observationStore.GetAllObservations(s.ctx)
+	if err != nil {
+		log.Error().Err(err).Msg("Failed to fetch observations for graph rebuild")
+		return
+	}
+
+	if len(observations) == 0 {
+		log.Debug().Msg("No observations to rebuild graph from")
+		return
+	}
+
+	// Rebuild graph
+	if err := s.graphSearchClient.RebuildGraph(s.ctx, observations); err != nil {
+		log.Error().Err(err).Msg("Failed to rebuild observation graph")
+		return
+	}
+
+	elapsed := time.Since(start)
+	stats := s.graphSearchClient.GetGraphStats()
+
+	log.Info().
+		Int("observations", len(observations)).
+		Int("nodes", stats.NodeCount).
+		Int("edges", stats.EdgeCount).
+		Float64("avg_degree", stats.AvgDegree).
+		Dur("elapsed", elapsed).
+		Msg("Observation graph rebuilt successfully")
+}
+
 // setupMiddleware configures HTTP middleware.
 func (s *Service) setupMiddleware() {
 	s.router.Use(middleware.Logger)
@@ -1106,6 +1263,10 @@ func (s *Service) setupRoutes() {
 		r.Get("/api/types", s.handleGetTypes)
 		r.Get("/api/models", s.handleGetModels)
 
+		// Graph and vector metrics routes
+		r.Get("/api/graph/stats", s.handleGetGraphStats)
+		r.Get("/api/vector/metrics", s.handleGetVectorMetrics)
+
 		// Observation scoring and feedback routes
 		r.Post("/api/observations/{id}/feedback", s.handleObservationFeedback)
 		r.Get("/api/observations/{id}/score", s.handleExplainScore)
@@ -1372,6 +1533,11 @@ func (s *Service) Shutdown(ctx context.Context) error {
 		s.patternDetector.Stop()
 	}
 
+	// Stop graph rebuild ticker
+	if s.graphRebuildTicker != nil {
+		s.graphRebuildTicker.Stop()
+	}
+
 	// Shutdown all sessions
 	s.sessionManager.ShutdownAll(ctx)
 

ui/package-lock.json

@@ -1,12 +1,12 @@
 {
   "name": "claude-mnemonic-dashboard",
-  "version": "40a44a7-dirty",
+  "version": "4f4b4ac-dirty",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "claude-mnemonic-dashboard",
-      "version": "40a44a7-dirty",
+      "version": "4f4b4ac-dirty",
       "dependencies": {
         "vis-data": "^7.1.9",
         "vis-network": "^9.1.9",

ui/package.json

@@ -1,6 +1,6 @@
 {
   "name": "claude-mnemonic-dashboard",
-  "version": "40a44a7-dirty",
+  "version": "4f4b4ac-dirty",
   "private": true,
   "type": "module",
   "scripts": {

ui/src/components/Sidebar.vue

@@ -2,6 +2,7 @@
 import { ref, computed } from 'vue'
 import type { Stats, SelfCheckResponse } from '@/types'
 import ProjectFilter from './ProjectFilter.vue'
+import { useGraphMetrics } from '@/composables'
 
 const props = defineProps<{
   stats: Stats | null
@@ -18,12 +19,21 @@ defineEmits<{
 
 // Collapse state - persisted in localStorage
 const isCollapsed = ref(localStorage.getItem('sidebar-collapsed') === 'true')
+const metricsExpanded = ref(localStorage.getItem('metrics-expanded') === 'true')
+
+// Graph metrics composable
+const { graphStats, vectorMetrics, loading: metricsLoading, refresh: refreshMetrics } = useGraphMetrics()
 
 function toggleCollapse() {
   isCollapsed.value = !isCollapsed.value
   localStorage.setItem('sidebar-collapsed', String(isCollapsed.value))
 }
 
+function toggleMetrics() {
+  metricsExpanded.value = !metricsExpanded.value
+  localStorage.setItem('metrics-expanded', String(metricsExpanded.value))
+}
+
 function formatNumber(n: number): string {
   if (n >= 1000000) return (n / 1000000).toFixed(1) + 'M'
   if (n >= 1000) return (n / 1000).toFixed(1) + 'K'
@@ -205,6 +215,99 @@ function getStatusColor(status: string): string {
         </div>
       </div>
 
+      <!-- Advanced Metrics -->
+      <div class="bg-slate-800/50 rounded-lg border border-slate-700/50">
+        <button
+          @click="toggleMetrics"
+          class="w-full flex items-center justify-between p-4 hover:bg-slate-700/30 transition-colors rounded-lg"
+        >
+          <div class="flex items-center gap-2">
+            <i class="fas fa-chart-line text-violet-400" />
+            <h3 class="text-sm font-semibold text-white">Advanced Metrics</h3>
+          </div>
+          <i
+            :class="[
+              'fas text-slate-400 transition-transform duration-200',
+              metricsExpanded ? 'fa-chevron-up' : 'fa-chevron-down'
+            ]"
+          />
+        </button>
+
+        <Transition name="expand">
+          <div v-show="metricsExpanded" class="px-4 pb-4 space-y-4">
+            <!-- Loading State -->
+            <div v-if="metricsLoading" class="text-center py-4">
+              <i class="fas fa-spinner fa-spin text-slate-400" />
+              <p class="text-slate-500 text-sm mt-2">Loading metrics...</p>
+            </div>
+
+            <!-- Graph Stats -->
+            <div v-else-if="graphStats?.enabled">
+              <div class="flex items-center justify-between mb-2">
+                <span class="text-xs text-slate-400 uppercase tracking-wide">Graph</span>
+                <button
+                  @click="refreshMetrics"
+                  class="text-xs text-violet-400 hover:text-violet-300 transition-colors"
+                  title="Refresh metrics"
+                >
+                  <i class="fas fa-sync-alt" />
+                </button>
+              </div>
+              <div class="space-y-2">
+                <div class="flex items-center justify-between">
+                  <span class="text-slate-400 text-sm">Nodes</span>
+                  <span class="text-white font-medium">{{ formatNumber(graphStats.nodeCount) }}</span>
+                </div>
+                <div class="flex items-center justify-between">
+                  <span class="text-slate-400 text-sm">Edges</span>
+                  <span class="text-white font-medium">{{ formatNumber(graphStats.edgeCount) }}</span>
+                </div>
+                <div class="flex items-center justify-between">
+                  <span class="text-slate-400 text-sm">Avg Degree</span>
+                  <span class="text-white font-medium">{{ graphStats.avgDegree.toFixed(1) }}</span>
+                </div>
+                <div class="flex items-center justify-between">
+                  <span class="text-slate-400 text-sm">Max Degree</span>
+                  <span class="text-white font-medium">{{ graphStats.maxDegree }}</span>
+                </div>
+              </div>
+
+              <!-- Vector Metrics -->
+              <div v-if="vectorMetrics?.enabled" class="mt-4 pt-4 border-t border-slate-700/50">
+                <div class="text-xs text-slate-400 uppercase tracking-wide mb-2">Vector Storage</div>
+                <div class="space-y-2">
+                  <div class="flex items-center justify-between">
+                    <span class="text-slate-400 text-sm">Savings</span>
+                    <span class="text-green-400 font-medium">
+                      {{ vectorMetrics.storage.savingsPercent.toFixed(1) }}%
+                    </span>
+                  </div>
+                  <div class="flex items-center justify-between">
+                    <span class="text-slate-400 text-sm">Queries</span>
+                    <span class="text-white font-medium">{{ formatNumber(vectorMetrics.queries.total) }}</span>
+                  </div>
+                  <div class="flex items-center justify-between">
+                    <span class="text-slate-400 text-sm">Cache Hit</span>
+                    <span class="text-cyan-400 font-medium">
+                      {{ (vectorMetrics.cache.hitRate * 100).toFixed(1) }}%
+                    </span>
+                  </div>
+                  <div class="flex items-center justify-between">
+                    <span class="text-slate-400 text-sm">Avg Latency</span>
+                    <span class="text-white font-medium text-xs">{{ vectorMetrics.latency.avg }}</span>
+                  </div>
+                </div>
+              </div>
+            </div>
+
+            <!-- Disabled State -->
+            <div v-else class="text-slate-500 text-sm py-2">
+              {{ graphStats?.message || 'Metrics not available' }}
+            </div>
+          </div>
+        </Transition>
+      </div>
+
       <!-- Session Info -->
       <div v-if="stats" class="bg-slate-800/50 rounded-lg p-4 border border-slate-700/50">
         <div class="flex items-center gap-2 mb-3">
@@ -260,6 +363,30 @@ function getStatusColor(status: string): string {
       >
         <i class="fas fa-search text-cyan-400" />
       </div>
+
+      <!-- Metrics indicator -->
+      <div
+        v-if="graphStats?.enabled"
+        class="bg-slate-800/50 rounded-lg p-3 border border-slate-700/50 flex justify-center"
+        :title="`${graphStats.nodeCount} nodes, ${graphStats.edgeCount} edges`"
+      >
+        <i class="fas fa-chart-line text-violet-400" />
+      </div>
     </div>
   </aside>
 </template>
+
+<style scoped>
+.expand-enter-active,
+.expand-leave-active {
+  transition: all 0.3s ease;
+  overflow: hidden;
+  max-height: 500px;
+}
+
+.expand-enter-from,
+.expand-leave-to {
+  max-height: 0;
+  opacity: 0;
+}
+</style>

ui/src/composables/index.ts

@@ -3,3 +3,4 @@ export { useStats } from './useStats'
 export { useTimeline } from './useTimeline'
 export { useUpdate } from './useUpdate'
 export { useHealth } from './useHealth'
+export { useGraphMetrics } from './useGraphMetrics'

ui/src/composables/useGraphMetrics.ts

@@ -0,0 +1,43 @@
+import { ref, onMounted } from 'vue'
+import type { GraphStats, VectorMetrics } from '@/types'
+import { fetchGraphStats, fetchVectorMetrics } from '@/utils/api'
+
+export function useGraphMetrics() {
+  const graphStats = ref<GraphStats | null>(null)
+  const vectorMetrics = ref<VectorMetrics | null>(null)
+  const loading = ref(false)
+  const error = ref<string | null>(null)
+
+  const refresh = async () => {
+    loading.value = true
+    error.value = null
+
+    try {
+      // Fetch both in parallel
+      const [graph, vector] = await Promise.all([
+        fetchGraphStats(),
+        fetchVectorMetrics()
+      ])
+
+      graphStats.value = graph
+      vectorMetrics.value = vector
+    } catch (err) {
+      error.value = err instanceof Error ? err.message : 'Failed to fetch metrics'
+      console.error('[GraphMetrics] Error:', err)
+    } finally {
+      loading.value = false
+    }
+  }
+
+  onMounted(() => {
+    refresh()
+  })
+
+  return {
+    graphStats,
+    vectorMetrics,
+    loading,
+    error,
+    refresh
+  }
+}

ui/src/types/api.ts

@@ -63,3 +63,58 @@ export interface SelfCheckResponse {
   uptime: string
   components: ComponentHealth[]
 }
+
+export interface GraphStats {
+  enabled: boolean
+  nodeCount: number
+  edgeCount: number
+  avgDegree: number
+  maxDegree: number
+  minDegree: number
+  medianDegree: number
+  edgeTypes: Record<string, number>
+  config: {
+    maxHops: number
+    branchFactor: number
+    edgeWeight: number
+    rebuildIntervalMin: number
+  }
+  message?: string
+}
+
+export interface VectorMetrics {
+  enabled: boolean
+  queries: {
+    total: number
+    hubOnly: number
+    hybrid: number
+    onDemand: number
+    graph: number
+  }
+  latency: {
+    avg: string
+    p50: string
+    p95: string
+    p99: string
+    avgHub: string
+    avgRecompute: string
+  }
+  storage: {
+    totalDocuments: number
+    hubDocuments: number
+    storedEmbeddings: number
+    savingsPercent: number
+    recomputedTotal: number
+  }
+  cache: {
+    hits: number
+    misses: number
+    hitRate: number
+  }
+  graph: {
+    traversals: number
+    avgDepth: number
+  }
+  uptime: string
+  message?: string
+}

ui/src/utils/api.ts

@@ -1,4 +1,4 @@
-import type { Observation, UserPrompt, SessionSummary, Stats, FeedItem, ObservationFeedItem, PromptFeedItem, SummaryFeedItem, RelationWithDetails, RelationGraph, RelationStats } from '@/types'
+import type { Observation, UserPrompt, SessionSummary, Stats, FeedItem, ObservationFeedItem, PromptFeedItem, SummaryFeedItem, RelationWithDetails, RelationGraph, RelationStats, GraphStats, VectorMetrics } from '@/types'
 
 const API_BASE = '/api'
 const DEFAULT_TIMEOUT = 10000 // 10 seconds
@@ -164,3 +164,11 @@ export async function fetchRelatedObservations(observationId: number, minConfide
 export async function fetchRelationStats(signal?: AbortSignal): Promise<RelationStats> {
   return fetchWithRetry<RelationStats>(`${API_BASE}/relations/stats`, { signal })
 }
+
+export async function fetchGraphStats(signal?: AbortSignal): Promise<GraphStats> {
+  return fetchWithRetry<GraphStats>(`${API_BASE}/graph/stats`, { signal })
+}
+
+export async function fetchVectorMetrics(signal?: AbortSignal): Promise<VectorMetrics> {
+  return fetchWithRetry<VectorMetrics>(`${API_BASE}/vector/metrics`, { signal })
+}

ui/tsconfig.tsbuildinfo

@@ -1 +1 @@
-{"root":["./src/main.ts","./src/vite-env.d.ts","./src/components/index.ts","./src/composables/index.ts","./src/composables/usehealth.ts","./src/composables/usesse.ts","./src/composables/usestats.ts","./src/composables/usetimeline.ts","./src/composables/usetypes.ts","./src/composables/useupdate.ts","./src/types/api.ts","./src/types/index.ts","./src/types/observation.ts","./src/types/prompt.ts","./src/types/relation.ts","./src/types/summary.ts","./src/utils/api.ts","./src/utils/formatters.ts","./src/app.vue","./src/components/badge.vue","./src/components/card.vue","./src/components/filtertabs.vue","./src/components/header.vue","./src/components/iconbox.vue","./src/components/observationcard.vue","./src/components/projectfilter.vue","./src/components/promptcard.vue","./src/components/relationgraph.vue","./src/components/sidebar.vue","./src/components/statscards.vue","./src/components/summarycard.vue","./src/components/timeline.vue"],"version":"5.7.3"}
+{"root":["./src/main.ts","./src/vite-env.d.ts","./src/components/index.ts","./src/composables/index.ts","./src/composables/usegraphmetrics.ts","./src/composables/usehealth.ts","./src/composables/usesse.ts","./src/composables/usestats.ts","./src/composables/usetimeline.ts","./src/composables/usetypes.ts","./src/composables/useupdate.ts","./src/types/api.ts","./src/types/index.ts","./src/types/observation.ts","./src/types/prompt.ts","./src/types/relation.ts","./src/types/summary.ts","./src/utils/api.ts","./src/utils/formatters.ts","./src/app.vue","./src/components/badge.vue","./src/components/card.vue","./src/components/filtertabs.vue","./src/components/header.vue","./src/components/iconbox.vue","./src/components/observationcard.vue","./src/components/projectfilter.vue","./src/components/promptcard.vue","./src/components/relationgraph.vue","./src/components/sidebar.vue","./src/components/statscards.vue","./src/components/summarycard.vue","./src/components/timeline.vue"],"version":"5.7.3"}