Knowledge Graph RAG explained in 5min

Understanding Knowledge Graph RAG: Elevating Retrieval-Augmented Generation In this video, we dive into Knowledge Graph RAG and compare it with vector database RAG for retrieval-augmented generation. We explore how vector DB RAG works by chunking a document into smaller pieces and creating semantic embeddings for each chunk, which are then used for search queries through cosine similarity. However, this approach lacks relational context between chunks. Conversely, Knowledge Graph RAG maintains these relationships, connecting chunks, books, and other entities like authors through nodes and edges in a graph. This method enhances retrieval capabilities, making it particularly useful for complex data relationships and is utilized by major search engines like Google and Bing. 00:00 Introduction to Knowledge Graph RAG 00:09 Understanding Vector DB RAG 00:35 Chunking and Embeddings 01:24 Limitations of Vector DB RAG 02:39 Introduction to Knowledge Graphs 03:03 Building Relationships in Knowledge Graphs 04:25 Advantages of Knowledge Graph RAG 04:37 Real-World Applications 05:10 Conclusion and Future Prospects