Advancing AI - Ep. 5 GraphRAG Recap and Summary
In our final episode, we'll recap the key points from the series in a concise 10-minute summary. Reflect on what you've learned about GraphRAG, revisit unique aspects of each implementation, and understand how to apply this knowledge to your projects. Make sure to revisit the previous episodes for a comprehensive understanding!
Resources:
The Knowledge Graph Difference: https://data.world/blog/generative-ai...
GraphAcademy - Neo4j: https://graphacademy.neo4j.com/
The DeepLearning.AI short course on Knowledge Graphs for RAG: https://www.deeplearning.ai/short-cou...
Microsoft GraphRAG: https://microsoft.github.io/graphrag/
🦜️🔗 LangChain: ttps://python.langchain.com/v0.1/docs/use_cases/graph/
LlamaIndex: https://docs.llamaindex.ai/en/stable/...
Graph RAG vs RAG vs NL2Cypher Demo: https://www.nebula-graph.io/posts/bui...
Deloitte’s paper titled Responsible Enterprise Decisions with Knowledge-Enriched Generative AI: https://www2.deloitte.com/nl/nl/pages...
Gartner’s 2024 Impact Radar for Generative AI: https://www.gartner.com/en/articles/u...
CAN GRAPHRAG FINALLY SOLVE THE KENDRICK & DRAKE DISPUTE?: https://www.advancinganalytics.co.uk/...
