A community-kiln booking handbook is stored as meaning vectors so “glaze hold time” can surface a nearby “cooling schedule” note. Which store supports that semantic retrieval?
Select an answer to reveal the explanation.
Short Explanation
Embeddings are like map pins for meaning — nearby ideas sit close together. A vector database holds those pins so “glaze hold time” can pull a related “cooling schedule” note. That is not the same as a relational case table.
Full Explanation
Vector databases store embeddings used for semantic retrieval of related content. They complement relational stores that hold structured test cases. In LLM-powered test infrastructure, the vector store enables meaning-based lookup of handbook material rather than exact row matches alone.