Graph Data with Neo4j for GraphRAG & Agent Context- AI Build & Learn #7
Details
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: https://github.com/sagecodes/ai-build-and-learn
- Events Calendar: https://luma.com/ai-builders-and-learners
- Slack (Discuss during the week): https://slack.flyte.org/
- Hosted by Sage Elliott: https://www.linkedin.com/in/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).