Knowledge Graphs for RAG without a GraphDB

Retrieval Augmented Generation (RAG) is a technique that uses information retrieval methods to provide contextual information for generative AI. However, RAG based on vector similarity has some weaknesses, such as difficulty in answering questions involving multiple topics and limitations on the number of chunks retrieved. Knowledge Graphs can be used as an alternative or supplement to vector-based chunk retrieval. In a knowledge graph, nodes correspond to specific entities, and edges indicate relationships between the entities. This approach has several benefits over the similarity-based approach, including better handling of multiple topics and nuances from different sources. Knowledge Graphs can be created using LLMs (Large Language Models) and stored in databases like DataStax Astra DB for efficient retrieval. The use of knowledge graphs for RAG does not require graph databases or specialized query languages, making it easier to apply using a typical data store.

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