Vector Database Use Cases: Search, RAG, and AI Apps
Couchbase, Tuesday, November 3rd, 2026
Couchbase explains what vector databases are and their main uses: semantic search, RAG, recommendations, chatbots and anomaly detection.
This Couchbase explainer covers how vector databases store embeddings and use ANN indexes such as HNSW and IVF to retrieve results by meaning rather than keyword match.
It walks through the main use cases: semantic search, retrieval-augmented generation (named as the main driver of enterprise adoption), recommendation systems, chatbots, anomaly and duplicate detection, and multimodal image and audio search.
It compares vector and traditional databases and argues they work best together.
It also covers architecture concerns such as embedding pipelines, latency and scale, and integration with LangChain and LlamaIndex.