Graph Data with Neo4j for GraphRAG & Agent Context- AI Build & Learn #7

​Welcome to AI Build & Learn a weekly AI engineering stream where we pick a new topic and learn by building together.

​This event is about building with Neo4j, a graph database, for RAG or Agent context applications.

​We'll explore how graph databases differ from vector databases, when to reach for knowledge graphs, and how to combine graph traversal with semantic search (GraphRAG) to give agents richer context.

​Some things to look up to get started:

• ​Neo4j: https://neo4j.com/

• ​Neo4j GraphRAG: https://neo4j.com/labs/genai-ecosystem/graphrag/

​Resources

• ​GitHub: /sagecodes/ai-build-and-learn

• ​Events Calendar: https://luma.com/ai-builders-and-learners

• ​Slack (Discuss during the week): Flyte Slack Group

• ​Hosted by Sage Elliott: /sageelliott

​In this stream

• ​Intro to topic

• ​Community Discussion

• ​Practical examples

​Community challenge (optional)

​Try spending 30–90 minutes during the week learning or building something related to the topic, then share what you’re working on in Slack.

​Note on Flyte / Union

​You may see Flyte used in some demos. Flyte is an open-source AI orchestration platform maintained by Union (where I work) for building scalable, durable, and observable AI workflows. You do not need to use Flyte to participate.

• ​Union: https://www.union.ai/

• ​Flyte: https://flyte.org/

​Drop a comment with ideas for future topics (agents, RAG, MLOps, robotics, frameworks, and more).