Set up your development environment

During this course, you will:

• Use the Neo4j GraphRAGRetrieval-augmented generation whose context comes from a knowledge graph, so the model can follow the relationships between facts. for Python (neo4j_graphrag) package to create a knowledge graphA representation of real-world entities and their relationships, stored according to organizing principles, typically in a graph database. from unstructured and structured data
• Create vector and text to CypherNeo4j's implementation of GQL, the ISO standard query language for graph databases. It is declarative: you describe the pattern to find, and the database decides how to find it. retrieversA component that searches a data source and returns the information relevant to a query. Often used to provide context for a language model. that use the knowledge graph to provide context to an LLMA model trained on text to predict the next token, and so to generate language.