Graphify

Graphify

Build knowledge graphs for LLM applications. Knowledge graphs improve AI responses by providing structured context with relationships, enable retrieval-augmented generation with graph traversal, and support agentic workflows with graph-defined tools.

When to Use

• Building knowledge bases with relationships (not just chunks)
• Implementing Graph RAG for better recall
• Extracting structure from unstructured documents
• Building agent memories with relationships
• Creating recommendation systems
• Analyzing code dependencies

1. Graph Storage Selection

Choose storage based on query patterns and scale requirements.

In-Memory Graph

Use for: prototyping, small graphs (<10K nodes), single-machine apps

// Example: GraphLib or native Map/Set
const graph = new Map<string, Set<string>>();

PostgreSQL with Extensions

Use when: already using PostgreSQL, need ACID compliance, moderate scale

• pggraph: Native graph support via extensions
• Works with existing Postgres infrastructure

Neo4j

Use when: complex relationship queries, Cypher proficiency, managed needed

• Best for: traversals, path finding, graph algorithms
• Avoid if: simple queries dominate

Redis

Use when: caching, real-time, ephemeral graphs

• Best for: session graphs, rate limiting, recent activity

AWS Neptune

Use when: managed, need Gremlin/SPARQL, AWS ecosystem

• Serverless option available
• Integration with AWS services

Decision Matrix:

2. Graph Algorithms

Select algorithm based on the question you're answering.

Traversal (BFS/DFS)

Use for: exploration, finding any path, connectivity

• BFS: Shortest unweighted path, level-by-level
• DFS: Deep exploration, cycle detection

// BFS for shortest path
function bfs(graph, start, goal) {
  const queue = [[start]];
  const visited = new Set([start]);
  while (queue.length) {
    const path = queue.shift();
    const node = path[path.length - 1];
    if (node === goal) return path;
    for (const neighbor of graph.get(node) || []) {
      if (!visited.has(neighbor)) {
        visited.add(neighbor);
        queue.push([...path, neighbor]);
      }
    }
  }
}

Shortest Path (Dijkstra, A*)

Use for: weighted routing, travel time, cost optimization

Centrality Measures

Use for: identifying important nodes

• PageRank: Importance via links/votes
• Betweenness: Bridge identification
• Degree: Direct influence

Community Detection

Use for: clustering, segmentation

• Louvain: Large-scale community detection
• Label Propagation: Fast clustering

When to Use Each:

3. Graph Extraction Sources

Extract graphs from different data sources.

From Documents (PDF, Markdown)

Process: chunk → extract entities → extract relationships

// Extract entities from text chunk
prompt = `Extract entities from: {chunk}
Entities as JSON: { "entities": [{"id": "...", "type": "...", "name": "..."}] }`;

From Code (AST Parsing)

Extract: imports, function calls, class relationships

// Dependency graph from imports
imports.map(file => ({
  source: file.path,
  targets: file.imports,
  type: 'imports'
}));

From Websites

Link graphs from HTML parsing

// Extract links
links = html.querySelectorAll('a[href]')
  .map(a => ({ source: pageUrl, target: a.href, type: 'links_to' }));

From SQL

Schema graphs: tables, columns, foreign keys

// Extract schema relationships
foreignKeys.map(fk => ({
  source: fk.fromTable,
  target: fk.toTable,
  type: 'references',
  via: fk.column
}));

From JSON/YAML

Configuration graphs

// Dependencies from package.json
deps.map(d => ({ source: 'package', target: d.name, type: 'depends_on' }));

4. LLM Graph Construction

Build graphs using LLMs for entity and relationship extraction.

Entity Extraction Prompt

Extract all entities from the following text.
For each entity, provide: id, type, name, description.

Text: {text}

Output as JSON array:

Relationship Extraction Prompt

Extract relationships between these entities.
For each relationship: source, target, type, confidence (0-1).

Entities: {entities}

Relationships:

Relationship Confidence

• Use LLM to provide confidence scores
• Filter by threshold (e.g., confidence > 0.7)
• Allow incremental updating

Semantic Search with Embeddings

// Embed entities for semantic search
entities.forEach(entity => {
  entity.embedding = embed(entity.name + ' ' + entity.description);
});

// Query: find similar entities
similar = vectorSearch(queryEmbedding, entities, topK: 10);

Incremental Graph Building

1. Process new document
2. Extract entities (match existing → link, new → add)
3. Extract relationships (add/update)
4. Update embeddings

5. LLM Graph Integration

Use graphs with LLMs for improved retrieval.

Graph RAG Pattern

Context from knowledge graph:
{graph_context}

Question: {question}

Based on the graph context above, answer:

Graph retrieval steps:

1. Convert question to graph query
2. Traverse relevant subgraphs
3. Include relationship context in prompt

Graph Tools for Agents

Define tools from graph structure:

// Graph-defined tools
const tools = graph.nodes.map(node => ({
  name: `query_${node.type}`,
  description: `Query ${node.type} entities`,
  parameters: { ... }
}));

Subagent Orchestration via Graph

// Route through graph
function orchestrate(query, graph) {
  const relevant = graph.query(query);
  const agent = selectAgent(relevant.type);
  return agent.execute(query, relevant.context);
}

Hybrid RAG: Vector + Graph

Execute both, combine results.

6. Graph Visualization

Choose visualization based on context.

Mermaid

For documentation, README files:

graph TD
    A[User] --> B[Login]
    B --> C[Dashboard]
    C --> D[Query Graph]
    D --> E[Results]

D3.js

For interactive web applications:

// D3 force-directed graph
const simulation = d3.forceSimulation(nodes)
  .force('link', d3.forceLink(links).id(d => d.id))
  .force('charge', d3.forceManyBody())
  .force('center', d3.forceCenter(width / 2, height / 2));

Graphviz (DOT)

For static diagrams:

digraph {
  User -> Login -> Dashboard
  Dashboard -> Query
  Query -> Graph
}

Selection Guide:

Process Summary

Step 1: Choose Storage

Start simple, upgrade as needed

Step 2: Extract Graph

• From documents → chunk + LLM extraction
• From code → AST parsing
• From existing data → schema extraction

Step 3: Build Incrementally

• Process documents
• Deduplicate entities
• Add relationships
• Update embeddings

Step 4: Integrate with LLM

• Graph RAG for retrieval
• Graph tools for agents

Step 5: Visualize

As needed for debugging/documentation

Common Mistakes

Verification

• Graph storage selected based on query patterns
• Algorithm chosen for actual questions
• Extraction working from primary sources
• Entity/relationship extraction prompts defined
• Graph RAG pattern implemented
• Visualization working for debugging

Anti-Rationalization Table