Build a Knowledge Graph

In this chapter, you will learn the exciting process of building a knowledge graph. A knowledge graph is like a web of interconnected ideas where each idea (or entity) has its own unique identity and relationships with other ideas.

We'll start by using a powerful tool called a language model to automatically find and extract entities from text. These entities are then stored as nodes in our graph, complete with their unique identifiers, names, and descriptions. The connections between these entities, known as relationships or edges, will also be captured and described.

The chapter dives into the technical details of how this extraction happens, including a system prompt that guides the language model on what to look for when analyzing text. You'll see examples of how to format your requests to the language model and handle its responses using JSON schemas.

Next, we’ll explore storing these entities and relationships in SurrealDB, a flexible database designed for handling graph data. We will walk through creating nodes (entities) and edges (relationships), ensuring that our knowledge graph is robust and ready for use.

By the end of this chapter, you'll have a solid understanding of how to build your own knowledge graph from scratch, opening up possibilities for applications like intelligent question answering systems or recommendation engines. Dive in and discover the power of interconnected data!