Vector indexes bigger than RAM are reshaping how relational databases power AI agents. A new wave blends Planetscale-style ideas with off-RAM vector stores to support agent-driven prompts that outgrow memory limits [1].
What Larger-Than-RAM Vector Indexes Enable Relational engines can store embeddings beyond RAM, letting systems reach bigger knowledge bases for agent workloads [1]. The idea—detailed in a Planetscale blog post—pairs traditional rows with scalable vector indexes to keep data accessible without sacrificing performance. This shift unlocks longer, more diverse prompts and richer context for AI agents [1].
Agents Demand More Than RAG Knowledge lives in a vector database and is retrieved with a search_knowledge tool, often hybrid (keyword + semantic) with re-ranking [2]. Dynamic instructions treat the system prompt as RAM while the knowledge base lives on disk, pulled in at runtime to tailor behavior for each task [2]. For example, a Text2SQL agent uses schemas and templates on demand rather than hard-coding them into prompts [2]. Adaptive learning and a scratchpad could let memory evolve with use [2].
Blended Architectures in Practice Architectures that blend relational engines with vector indexes support agent-driven workloads and dynamic prompts, marrying speed with scale [1][2]. The pattern suggests flexible memory models: fast core logic in RAM, larger knowledge graphs on disk, and iterative searches that refine results over time [2].
Closing thought: watch for these hybrids as they mature, since they hint at a future where databases and agents share memory across layers.
References
Larger Than RAM Vector Indexes for Relational Databases
Examines vector indexes for relational databases beyond RAM, exploring scalability, performance, and storage trade-offs.
View sourceAgent Knowledge Needs More Than Just RAG
Vector databases power agents' knowledge as runtime memory; dynamic prompts and adaptive learning offer alternatives to static RAG patterns.
View source