Graph RAG and Agentic RAG (Part 2): Where Retrieval Finally Gets Smart

1. 1Build a Graph RAG model to improve retrieval for multi-hop questions
2. 2Implement Agentic RAG to track entity relationships across a large corpus
3. 3Configure a vector database to store and query graph embeddings
4. 4Test the performance of Graph RAG and Agentic RAG on a benchmark dataset
5. 5Apply these models to real-world applications such as question answering and entity disambiguation

Machine learning engineers and researchers can benefit from this article to improve their retrieval models, especially those working on question answering and entity relationship tracking tasks.

💡 Graph RAG and Agentic RAG can significantly improve retrieval performance for complex queries by incorporating graph structures and agentic behaviors

🤖 Improve retrieval for multi-hop questions and entity relationships with Graph RAG and Agentic RAG! 🚀