Vector search is a way of finding related information by semantic similarity instead of only exact keyword matching.
In practical AI systems, vector search helps the stack find the most relevant pieces of data so an application can retrieve better context before answering or acting.
If you hear teams talk about embeddings, retrieval, or memory in AI workflows, vector search is often part of what makes that possible.
Why teams use vector search
- to search documents by meaning instead of only keywords
- to support RAG-style assistants and knowledge systems
- to help tools such as OpenClaw retrieve better context
- to make AI workflows more useful with business data
Where it fits in TryDirect
Vector search usually appears through tools such as Qdrant and becomes one more important supporting layer inside an AI stack.