LLM Knowledge Graphs: The Developer's Secret Weapon

Knowledge graphs are becoming indispensable in the AI era. They provide structure to unstructured data, enhancing the accuracy and context of large language models (LLMs).

Developers are increasingly pairing knowledge graphs with LLMs for

• Reducing Hallucinations: Knowledge graphs provide a factual backbone, reducing inaccuracies in LLM outputs.
• Improved Context: Traditional chunking methods can miss crucial connections. Knowledge graphs maintain relationships between concepts, offering a more holistic view.
• Efficient Information Retrieval: By walking the graph, LLMs can retrieve relevant information more effectively than with simple text-based lookups.
• Semantic Understanding: Knowledge graphs capture the meaning and relationships between entities, enabling more nuanced comprehension.
• Lightweight Structure: Knowledge graphs are typically lightweight, allowing developers to send more context to LLMs without significantly increasing costs. This results in better outputs while keeping API bills manageable.

General Schematics: A New Approach to Knowledge Graphs

While developers often turn to tools like Neo4j or LlamaIndex for knowledge graph construction, there's a simpler way: General Schematics.

General Schematics is an information language that underpins Think Machine. It's a Markdown superset designed for creating concept maps and hypertext simultaneously.

• Markdown-Based: Familiar syntax for easy adoption.
• Trail Links: Inspired by Memex trails, these create semantic connections between concepts.
• Programmatic Creation: Easily generate knowledge graphs through code.

Creating Knowledge Graph connections in General Schematics is really easy, just add -> between concepts:

Concept A -> Concept B -> Concept C

This simple syntax creates a powerful semantic link, forming the basis of a knowledge graph.

Think Machine: General Schematics in Action

Think Machine is the practical application of General Schematics. It's an information IDE that combines hypertext, knowledge graphs, and AI.

HyperEditor

Create both concept maps and networked notes in a simple text editor.

Trail Links

Visually connect ideas, forming the knowledge graph structure.

Interwingle

Adjust the complexity and interconnectedness of your knowledge graph on the fly.

3D Visualization

Explore your knowledge graph in three-dimensional space.

AI Integration

Leverage LLMs to explore and expand your knowledge graph.

For developers, Think Machine offers a unique opportunity to visually construct and manipulate knowledge graphs without sacrificing programmatic control.

Try Think Machine

Knowledge graphs are reshaping how we interact with LLMs. General Schematics and Think Machine offer a fresh approach, blending visual intuition with programmatic power. Whether you're a developer looking to enhance LLM accuracy or a researcher aiming to map complex information, these tools provide a robust foundation for the future of AI-assisted knowledge work.

To learn more about how Think Machine can revolutionize your research and development process, check out our blog on AI research tools and the evolution of knowledge management.

Ready to dive in? Try Think Machine today and experience the power of LLM knowledge graphs firsthand.

Additional Resources

• Documentation on Knowledge Graphs
• Learn about 3D Mind Maps
• Explore Networked Notes
• Discover our Research Workspace
• Understanding HyperEditor
• Trail Links Explained
• General Schematics Documentation

For business applications of Think Machine, check out our business use cases.

New to some terms? Visit our Glossary for clarification.