Episode 27 of Going Meta – a series on graphs, semantics and knowledge

Jesús Barrasa: https://twitter.com/BarrasaDV
Repository: https://github.com/jbarrasa/goingmeta
Knowledge Graph Book: https://bit.ly/3LaqE6b
Previous Episodes: https://development.neo4j.dev/video/going-meta-a-series-on-graphs-semantics-and-knowledge/

Links:
GenAI Ecosystem: ttps://bit.ly/4cpAY6D
Is RAG dead? https://docs.google.com/presentation/d/1mJUiPBdtf58NfuSEQ7pVSEQ2Oqmek7F1i4gBwR6JDss/edit#slide=id.g26c0cb8dc66_0_131
What’s next for AI agentic workflows (Andrew Ng): https://www.youtube.com/watch?v=sal78ACtGTc
Reflection Agents: https://blog.langchain.dev/reflection-agents/
LangGraph: https://python.langchain.com/docs/langgraph
Supply Chain Dataset: https://www.kaggle.com/datasets/shashwatwork/dataco-smart-supply-chain-for-big-data-analysis
Crime Dataset: https://www.kaggle.com/datasets/sahirmaharajj/crime-data-from-2020-to-present-updated-monthly
Dataflow BigQuery: https://development.neo4j.dev/docs/dataflow-bigquery/

0:00 Intro
18:30 Reflection Agents with SupplyChain Data in a Notebook
35:00 Orchestrate with LangGraph
47:24 Same Logic with Crime Data
56:00 Summary and Outlook
1:00:49 Wrap-Up

#neo4j #graphdatabase #knowledgegraph #langchain #llm #genai #rag #graphrag