Skip to content- Comprehensive knowledge of the Neo4j Graph Data Science Library
- Graph Data Science workflows
- Backup and restore strategies
- Instance monitoring and metrics
- Resource optimization
- Query logs and performance tuning
- AuraDB tiers
- Instance management
- Cost optimization
- Data import and querying
- Backup and restore
- Convert unstructured data into a knowledge graph
- Customize a Knowledge Graph Schema
- Query a knowledge graph
- Using LLMs to generate Cypher queries
- Cypher patterns
- Reading data from a graph
- Writing data to a graph
- Graph data modeling fundamentals
- Creating graphs
- Graph Refactoring
- Graph Data Science
- Graph projections
- Installation options
- GDS licensing
- Importing unstructured data into graphs
- Vector indexes
- Embedding data
- Chunking
- Using Neo4j with LangChain
- Generative AI Fundamentals
- Large Language Models
- RAG
- GraphRag
- Integrating Neo4j with Generative AI
- Graph Data Science
- Graph algorithms
- Machine learning pipelines
- GDS machine learning operations