Knowledge Tree
Relations (1)
related 2.32 — strongly supporting 4 facts
RAG systems rely on vector databases as a core infrastructure component to store and retrieve numerical embeddings of document chunks, as described in [1] and [2]. Furthermore, vector databases are explicitly identified as a key configuration choice when evaluating RAG application performance [3] and are the standard storage solution for RAG integration [4].
Facts (4)
Sources
Knowledge Graphs vs RAG: When to Use Each for AI in 2026 - Atlan atlan.com 3 facts
claimKnowledge graph integration requires a graph database such as Neo4j or Amazon Neptune, while RAG integration works with vector stores such as Pinecone or Weaviate.
procedureTraditional RAG systems process documents by splitting them into chunks, converting those chunks into numerical embeddings, and storing them in vector databases.
claimRAG systems require minimal infrastructure, specifically a vector database, an embedding model, and a retrieval pipeline.
Evaluating RAG applications with Amazon Bedrock knowledge base ... aws.amazon.com 1 fact
claimThe Amazon Bedrock knowledge base evaluation feature allows users to assess RAG application performance by analyzing how different components, such as knowledge base configuration, retrieval strategies, prompt engineering, model selection, and vector store choices, impact metrics.