Semantic-Search

A vector database stores vectors alongside identifiers and metadata, indexes them for similarity search, and provides database capabilities such as persistence, filtering, updates, access patterns, and operational management. Its purpose is broader than running a nearest-neighbour algorithm. That distinction separates a database from a library such as FAISS. FAISS provides efficient vector indexing and search primitives. It does not, by itself, provide the complete persistence, multi-tenancy, metadata, authorisation, backup, replication, and service-management layer expected from a production database. ...

A knowledge graph represents entities and the relationships between them in a graph-shaped data model. Its value is not that graphs automatically contain truth or make systems explainable. The value is that identity, relationships, provenance, and domain meaning can be made explicit and queried together. That distinction matters in applied AI. A graph built from noisy documents can preserve extraction errors. A graph query can return an irrelevant path. An ontology can support valid inference from false assertions. A language model can still misread correctly retrieved facts. Knowledge graphs create useful structure; reliability comes from the evidence, controls, and evaluation around that structure. ...