Knowledge Graph RAG: two query patterns for smarter AI agents

Retrieval-Augmented Generation (RAG) has evolved with the integration of knowledge graphs, enhancing AI agents' ability to understand the relationships between concepts, chunks, and documents, a capability referred to as a context graph. This approach allows for more nuanced queries, enabling the retrieval of documents based on concept similarity rather than just matching text chunks. SurrealQL, the query language used, supports multi-model queries that combine vector, graph, relational, and BM25 models, facilitating sophisticated data retrieval without switching databases. Two main query patterns are highlighted: concept-based document retrieval, which leverages semantic similarity for document relevance, and direct chunk retrieval, which focuses on precise text-level matches. SurrealDB's features, such as built-in vector indexes, graph traversal without JOINs, and flexible query customization, make it a powerful tool for implementing RAG systems. The platform's cloud service and graphical IDE, Surrealist, further simplify managing and developing complex knowledge graphs, making it accessible for applications ranging from chatbots to autonomous agents.