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Knowledge Graph Guide

Varun Pratap Bhardwaj edited this page May 24, 2026 · 2 revisions

Knowledge Graph Guide

How knowledge graphs work in SuperLocalMemory - TF-IDF entity extraction, Leiden clustering, and graph-enhanced search explained for developers.

What is a Knowledge Graph?

A knowledge graph is a network of entities (concepts) and relationships that represents how your memories connect to each other. SuperLocalMemory automatically builds this graph from your saved memories to improve search quality and discover hidden relationships.

Example:

Memory 1: "We use FastAPI for REST APIs"
Memory 2: "JWT tokens expire after 24 hours"
Memory 3: "FastAPI requires authentication middleware"
Knowledge Graph discovers:
 FastAPI ←→ REST APIs
 FastAPI ←→ authentication
 authentication ←→ JWT tokens
Even though Memory 1 and 2 don't mention each other,
the graph connects them via "authentication"!

How It Works

SuperLocalMemory uses GraphRAG (Microsoft Research) approach with three core algorithms:

1. TF-IDF Entity Extraction

What it does: Identifies important terms (entities) in your memories.

TF-IDF = Term Frequency - Inverse Document Frequency

Formula (simplified):

×ば぀ log(total memories / memories with this term)">
importance = (how often term appears in memory)
 ×ば぀ log(total memories / memories with this term)

Example:

Memory: "FastAPI is faster than Flask for high-throughput APIs"
Extracted entities:
- "FastAPI" (TF-IDF: 0.85) βœ… Important
- "Flask" (TF-IDF: 0.72) βœ… Important
- "high-throughput" (TF-IDF: 0.68) βœ… Important
- "APIs" (TF-IDF: 0.45) ⚠️ Common but relevant
- "is" (TF-IDF: 0.02) ❌ Stop word, filtered out
- "than" (TF-IDF: 0.01) ❌ Stop word, filtered out

Filtering rules:

  • Minimum TF-IDF score: 0.1
  • Stop words removed (the, and, or, is, etc.)
  • Case insensitive ("React" = "react")
  • Minimum term length: 3 characters

2. Leiden Clustering Algorithm

What it does: Groups related memories into topic clusters.

Leiden = Community detection algorithm (better than older Louvain algorithm)

How it works:

  1. Creates graph nodes from entities
  2. Creates edges between entities that co-occur
  3. Detects "communities" (groups of highly connected nodes)
  4. Optimizes for modularity (how well-defined clusters are)

Example clusters discovered:

Cluster 1: "Authentication & Security" (23 memories)
 Top entities: JWT, OAuth, tokens, auth, security
Cluster 2: "Database & PostgreSQL" (18 memories)
 Top entities: PostgreSQL, database, SQL, queries, indexes
Cluster 3: "React & Frontend" (15 memories)
 Top entities: React, hooks, components, state, props

Modularity score:

  • Excellent: >0.7 (clusters are well-defined)
  • Good: 0.5-0.7 (clusters are meaningful)
  • Poor: <0.3 (clusters are arbitrary)

3. Relationship Discovery

What it does: Finds connections between memories.

Three types of edges:

A. Similarity Edges

cosine_similarity = dot(vector_A, vector_B) / (norm(vector_A) * norm(vector_B))
  • Score 0.8-1.0: Very similar content
  • Score 0.5-0.8: Related content
  • Score 0.3-0.5: Loosely related
  • Score <0.3: Not connected

B. Co-occurrence Edges

If two entities appear in same memory β†’ create edge
Weight = number of co-occurrences

C. Temporal Edges

If two memories created within 1 hour β†’ may be related
Useful for conversation threads

Building the Graph

Basic Build

slm build-graph

Output:

πŸ”„ Building Knowledge Graph...
Phase 1: Entity Extraction
 Scanning 1,247 memories...
 Extracted 892 unique entities
 Created 892 graph nodes
 βœ“ Complete (3.2s)
Phase 2: Relationship Discovery
 Computing similarity scores...
 Created 3,456 edges (relationships)
 Avg edges per node: 3.9
 βœ“ Complete (5.1s)
Phase 3: Optimization
 Indexing graph structure...
 Pruning weak edges (score < 0.3)...
 Final edge count: 2,134
 βœ“ Complete (1.2s)
βœ… Knowledge graph built successfully!
Graph Statistics:
 Nodes: 892
 Edges: 2,134
 Density: 0.27%
 Largest Component: 856 nodes (96%)

Build with Clustering

slm build-graph --clustering

Requires optional dependencies:

pip3 install python-igraph leidenalg

Additional output:

Phase 4: Topic Clustering (Leiden)
 Detecting communities...
 Found 47 clusters
 Largest cluster: 89 memories
 Smallest cluster: 3 memories
 Modularity score: 0.82 (excellent)
 βœ“ Complete (2.3s)
Discovered Clusters:
 Cluster 1 (89 memories): "Authentication & Security"
 Top entities: JWT, OAuth, tokens, auth, security
 Cluster 2 (76 memories): "Database & PostgreSQL"
 Top entities: PostgreSQL, database, SQL, queries, indexes

Force Rebuild

slm build-graph --force

Deletes existing graph and rebuilds from scratch. Use when:

  • Graph seems corrupted
  • After major bulk import
  • Want fresh start

Graph Statistics Explained

Node Count

Total unique entities extracted

Good indicators:

  • 100+ nodes for 1,000 memories
  • 500+ nodes for 5,000 memories

Poor indicators:

  • <10 nodes for 1,000 memories (not extracting entities properly)

Edge Count

Total relationships discovered

Edges/Nodes ratio:

  • Good: >2 (well-connected)
  • Poor: <1 (disconnected graph)

Example:

892 nodes, 2,134 edges
Ratio: 2,134 / 892 = 2.39 βœ… Good

Density

How connected the graph is

Formula:

×ば぀ 100 possible edges = nodes ×ば぀ (nodes - 1) / 2">
density = (actual edges / possible edges) ×ば぀ 100
possible edges = nodes ×ば぀ (nodes - 1) / 2

Example:

×ば぀ 891 / 2 = 397,386 Actual edges: 2,134 Density: (2,134 / 397,386) ×ば぀ 100 = 0.54%">
892 nodes
Possible edges: 892 ×ば぀ 891 / 2 = 397,386
Actual edges: 2,134
Density: (2,134 / 397,386) ×ば぀ 100 = 0.54%

Typical values:

  • 0.1% - 1%: Normal
  • <0.05%: Very disconnected (isolated knowledge)

  • 5%: Too connected (poor entity extraction)

Largest Component

Size of biggest connected subgraph

Good indicators:

  • 80% of nodes (knowledge is interconnected)

Poor indicators:

  • <50% of nodes (fragmented knowledge islands)

Example:

892 nodes total
856 nodes in largest component
Coverage: 856 / 892 = 96% βœ… Excellent

When to Rebuild Graph

Always Rebuild After:

  1. Bulk imports - Added 50+ memories at once
  2. Database restore - Restored from backup
  3. Major milestone - Sprint complete, project phase done

Rebuild Periodically:

  1. Monthly - Keep graph optimized
  2. After 500 new memories - Maintain quality
  3. When search feels slow - Rebuild indexes

Rebuild on Issues:

  1. Poor search results - Graph may be stale
  2. Missing relationships - Rebuild connections
  3. Corrupted graph errors - Force rebuild

Automation (cron):

# Every Sunday at 3 AM
0 3 * * 0 /usr/local/bin/slm build-graph --clustering >> /var/log/slm-build.log 2>&1

Graph-Enhanced Search

Without Graph

Basic keyword matching:

slm recall "authentication"
Results:
- "JWT tokens expire after 24 hours" βœ… Contains "auth" stem
- "User login endpoint uses POST" ❌ Missed (no "auth" keyword)

With Graph

Graph traversal finds related memories:

slm recall "authentication"
Results (via graph):
- "JWT tokens expire after 24 hours" βœ… Direct match
- "User login endpoint uses POST" βœ… Graph: login β†’ auth β†’ JWT
- "OAuth 2.0 flow implementation" βœ… Graph: OAuth β†’ tokens β†’ auth
- "Session management strategy" βœ… Graph: sessions β†’ auth β†’ security

How it works:

  1. Find memories matching query (direct)
  2. Extract entities from those memories
  3. Traverse graph to find related entities
  4. Find memories containing related entities
  5. Rank by combined score (keyword + graph + semantic)

Advanced Features

Cluster-Based Search

# Build with clustering
slm build-graph --clustering
# Search within specific cluster
slm recall "performance" --cluster "Database & PostgreSQL"

Benefits:

  • Faster search (smaller search space)
  • More relevant results (topically focused)
  • Avoids false positives from other domains

Related Memory Discovery

# Python API
from memory_store_v2 import MemoryStoreV2
from graph_engine import GraphEngine
store = MemoryStoreV2()
graph = GraphEngine()
# Find memories related to ID 42
related = graph.get_related_memories(42, limit=5)
for mem_id, score in related:
 print(f"Memory {mem_id}: {score:.2f}")

Graph Visualization (Planned v2.2.0)

# Export graph for visualization (coming soon)
slm build-graph --export graph.json
# Generate HTML visualization
slm graph-viz graph.json > graph.html

Performance Benchmarks

Build Time

Memory Count Build Time With Clustering
100 ~1s ~1.5s
1,000 ~10s ~15s
5,000 ~1min ~1.5min
10,000 ~2min ~3min
50,000+ ~15min ~25min

Factors affecting speed:

  • Memory content length (longer = slower)
  • Vocabulary size (more unique words = slower)
  • Hardware (CPU, RAM)

Search Improvement

Before graph:

  • Average search time: 150ms
  • Recall@10: 68% (finds 68% of relevant memories)

After graph:

  • Average search time: 45ms (×ば぀ faster!)
  • Recall@10: 87% (finds 87% of relevant memories)

Improvement: 28% more relevant results, 70% faster

Troubleshooting

"Build failed: Memory error"

Cause: Not enough RAM for large graph

Solution:

# Build in chunks
slm build-graph --chunk-size 1000
# Or archive old memories first
sqlite3 ~/.claude-memory/memory.db \
 "DELETE FROM memories WHERE created_at < date('now', '-180 days');"

"Clustering requires python-igraph"

Cause: Optional dependencies not installed

Solution:

pip3 install python-igraph leidenalg
# Verify
python3 -c "import igraph; import leidenalg"
# Try again
slm build-graph --clustering

"Edges seem wrong"

Cause: Stale graph or poor similarity threshold

Solution:

# Force complete rebuild
slm build-graph --force
# Adjust similarity threshold (advanced)
slm build-graph --min-similarity 0.4 # Default: 0.3

"Graph build slow"

Solutions:

# Show progress
slm build-graph --verbose
# Skip clustering (faster)
slm build-graph # No --clustering flag
# Check disk space
df -h ~/.claude-memory/

Best Practices

1. Build After Bulk Operations

# Import many memories
while read -r line; do
 slm remember "$line"
done < bulk_memories.txt
# Immediately rebuild graph
slm build-graph

2. Use Clustering for Large Databases

# Install dependencies once
pip3 install python-igraph leidenalg
# Always build with clustering if >1000 memories
if [ $(slm status | grep "Total memories" | awk '{print 3ドル}') -gt 1000 ]; then
 slm build-graph --clustering
else
 slm build-graph
fi

3. Monitor Graph Quality

# Check graph statistics
slm status --verbose | grep -A 10 "Knowledge Graph"
# Good indicators:
# - Edges/Nodes ratio > 2
# - Density: 0.1% - 1%
# - Largest component: >80%
# - Modularity (if clustering): >0.5

4. Automate Rebuilds

# Add to crontab
# Weekly: Sunday 3 AM
0 3 * * 0 /usr/local/bin/slm build-graph --clustering
# After git push (post-push hook)
#!/bin/bash
slm remember "Pushed $(git log -1 --oneline)" --tags git
slm build-graph

Technical Deep Dive

TF-IDF Implementation

Python code (simplified):

from sklearn.feature_extraction.text import TfidfVectorizer
# Extract entities
vectorizer = TfidfVectorizer(
 max_features=5000,
 min_df=2,
 max_df=0.8,
 stop_words='english',
 ngram_range=(1, 2)
)
# Fit on all memories
tfidf_matrix = vectorizer.fit_transform(memories)
# Get feature names (entities)
entities = vectorizer.get_feature_names_out()
# Filter by score threshold
important_entities = [e for e, score in zip(entities, scores) if score > 0.1]

Leiden Algorithm Parameters

Resolution parameter:

  • Default: 1.0
  • Lower (0.5): Fewer, larger clusters
  • Higher (2.0): More, smaller clusters

Quality metric (modularity):

Q = (edges_within_clusters / total_edges) - (expected_edges_within_clusters / total_edges)2

Edge Pruning

Remove weak edges to improve performance:

# Keep only edges with score > threshold
threshold = 0.3
pruned_edges = [(u, v, w) for u, v, w in edges if w > threshold]
# Result: 30-50% fewer edges, same search quality

Hierarchical Leiden Clustering (v2.4.1)

Standard Leiden finds flat communities β€” "Python", "JavaScript", "DevOps". Hierarchical Leiden goes deeper by recursively sub-clustering large communities:

Python (42 members)
β”œβ”€β”€ FastAPI (18 members)
β”‚ β”œβ”€β”€ Authentication (7 members)
β”‚ └── Database Models (6 members)
β”œβ”€β”€ Data Science (14 members)
└── CLI Tools (10 members)

How It Works

  1. Flat Leiden runs first (existing behavior)
  2. Clusters with β‰₯10 members are recursively sub-clustered
  3. Maximum depth: 3 levels (configurable via max_depth parameter)
  4. Each sub-cluster gets its own name from TF-IDF entity extraction
  5. parent_cluster_id and depth columns track the hierarchy in graph_clusters table

CLI

# Run hierarchical sub-clustering on existing clusters
python3 ~/.claude-memory/graph_engine.py hierarchical
# Full build (includes hierarchical + summaries automatically)
python3 ~/.claude-memory/graph_engine.py build

Schema

-- New columns on graph_clusters (added automatically)
ALTER TABLE graph_clusters ADD COLUMN parent_cluster_id INTEGER;
ALTER TABLE graph_clusters ADD COLUMN depth INTEGER DEFAULT 0;
ALTER TABLE graph_clusters ADD COLUMN summary TEXT;

Community Summaries (v2.4.1)

Every cluster gets a TF-IDF structured summary describing its contents:

Cluster "FastAPI & Authentication"
Summary: Key topics: fastapi, authentication, jwt, middleware, oauth |
 Projects: myapp, api-gateway | Categories: backend |
 18 memories | Sub-cluster of: Python

What's in a Summary

Component Source Example
Key topics Top 5 TF-IDF entities fastapi, authentication, jwt
Projects Distinct project_name values myapp, api-gateway
Categories Distinct category values backend, security
Size Member count 18 memories
Hierarchy Parent cluster name (if sub-cluster) Sub-cluster of: Python

CLI

# Generate summaries for all clusters
python3 ~/.claude-memory/graph_engine.py summaries
# Summaries are also generated automatically during build
python3 ~/.claude-memory/graph_engine.py build

Summaries appear in the web dashboard clusters view and are returned by the /api/clusters endpoint.

Related Pages

Created by Varun Pratap Bhardwaj Solution Architect β€’ SuperLocalMemory

GitHub β€’ Issues β€’ Wiki

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