RKnowledge – Knowledge Graph Builder

Build knowledge graphs from any text corpus using LLMs. This skill helps you extract concepts and relationships from documents and store them in a queryable graph database.

When to Use

Use this skill when you need to:

• Extract knowledge from documents (PDF, Markdown, HTML, TXT)
• Build a knowledge graph for Graph RAG applications
• Analyze relationships between concepts in a corpus
• Create visual representations of document content
• Query extracted knowledge using natural language or Cypher

Quick Start

1. Initialize

# Install and initialize rknowledge
rknowledge init

This creates a configuration file and starts Neo4j via Docker.

2. Configure API Keys

Use the auth command to configure your LLM provider:

# Interactive setup
rknowledge auth

# Or specify provider directly
rknowledge auth --provider anthropic

# Or set directly with key
rknowledge auth --provider anthropic --key your-key-here

# List configured providers
rknowledge auth --list

Alternatively, use environment variables:

export ANTHROPIC_API_KEY=your-key-here
# or
export OPENAI_API_KEY=your-key-here
# or use Ollama for local models (no API key needed)

3. Build Knowledge Graph

# Process a single document
rknowledge build ./document.pdf

# Process a directory of documents
rknowledge build ./docs/

# Specify provider and model
rknowledge build ./docs/ --provider anthropic --model claude-sonnet-4-20250514

4. Query the Graph

# Natural language search
rknowledge query "What concepts relate to authentication?"

# Direct Cypher query
rknowledge query "cypher: MATCH (n)-[r]->(m) RETURN n, r, m LIMIT 10"

5. Export

# Export to JSON
rknowledge export --format json --output graph.json

# Export to CSV (creates nodes.csv and edges.csv)
rknowledge export --format csv --output graph

# Export to GraphML
rknowledge export --format graphml --output graph.graphml

# Export to Cypher statements
rknowledge export --format cypher --output import.cypher

6. Visualize

# Open interactive visualization in browser
rknowledge viz

Commands Reference

Build Options

Supported File Types

• PDF (.pdf) – Extracts text from PDF documents
• Markdown (.md) – Parses and extracts text from Markdown
• HTML (.html, .htm) – Extracts text content from HTML
• Plain Text (.txt) – Direct text processing

LLM Providers

Anthropic (Recommended)

export ANTHROPIC_API_KEY=your-key
rknowledge build ./docs --provider anthropic

OpenAI

export OPENAI_API_KEY=your-key
rknowledge build ./docs --provider openai

Google (Gemini)

export GOOGLE_API_KEY=your-key
rknowledge build ./docs --provider google

Ollama (Local)

# Start Ollama first
ollama run mistral
rknowledge build ./docs --provider ollama --model mistral

Example Workflows

Build a Knowledge Base from Documentation

# Clone a repo's docs
git clone https://github.com/example/project docs

# Build knowledge graph
rknowledge build ./docs --provider anthropic

# Query for specific topics
rknowledge query "How does authentication work?"

Analyze Research Papers

# Process PDF papers
rknowledge build ./papers/ --chunk-size 2000

# Export for further analysis
rknowledge export --format json --output research-graph.json

Create Graph RAG Backend

# Build comprehensive graph
rknowledge build ./knowledge-base/

# Query programmatically via Neo4j
# Connect to bolt://localhost:7687 with neo4j/rknowledge

Neo4j Access

After running rknowledge init, Neo4j is available at:

• Browser: http://localhost:7474
• Bolt: bolt://localhost:7687
• Credentials: neo4j / rknowledge

Troubleshooting

Neo4j Connection Failed

# Check if Docker is running
docker ps

# Restart Neo4j
cd ~/.config/rknowledge
docker compose up -d

API Key Issues

# Verify API key is set
echo $ANTHROPIC_API_KEY

# Or check config file
cat ~/.config/rknowledge/config.toml

Large Documents

For very large documents, increase chunk size:

rknowledge build ./large-doc.pdf --chunk-size 3000 --chunk-overlap 300

See Also

• REFERENCE.md – Detailed configuration reference
• Neo4j Documentation
• Cypher Query Language