Scalable AI search
Hyperscale vector index for billion-scale data and is ideal for RAG, agents, and recommendations applications.
Couchbase 8.0 lets developers move from concept to production AI faster than ever. They can build fast AI-powered applications with huge datasets at low TCO, get started quickly with natural language queries, and ensure quick queries with new troubleshooting tools.
Use natural language to query with SQL++ extensions.
Search with developer-defined synonyms to have a smarter search.
Built-in workload repository and performance insights.
Compatible with popular AI frameworks.
Improving operational excellence with smarter cluster management, advanced security capabilities, dynamic rebalancing, and faster failover for continuous service availability.
Out-of-the-box native encryption at rest makes data safer and life simpler.
Dynamically adjust non-KV services without adding or removing nodes, eliminating rebalance delays.
Aggregate information from your SDK client telemetry for improved end-to-end monitoring and faster troubleshooting.
Auto-failover of non-responsive data nodes to improve application uptime. Serve requests while caches warm up.
It supports billions of vectors with millisecond retrieval speeds using a DiskANN-based design.
Encryption at rest, KMIP key management, and event monitoring ensure data integrity and compliance.
Yes. Use the search vector index for hybrid vector + lexical queries.
Automatic failover, rebalancing, and faster startup ensure continuous operations.
By reviewing the Couchbase 8.0 announcement blog.
Hyperscale helps RAG-style use cases when you cannot anticipate what a prompt is going to ask an LLM, while Composite vector indexes use prefiltering parameters to narrow the vectors to include in a prompt. Both offer millisecond response so RAG workflows do not slow down.
Achieve high performance and accuracy at billion-scale vector for AI agents, RAG workflows, contextual memory and recommendation systems – on premises or in Capella.