Vector database
A vector database is a store designed to hold embeddings and find the ones most similar to a query embedding quickly.
It is the retrieval half of RAG. Retrieval quality depends far more on how documents were chunked and what metadata was stored alongside them than on which database was chosen.
Related terms
- Embedding
- An embedding is a list of numbers representing a piece of text’s meaning, so that texts with similar meanings sit close together in that numeric space.
- RAG (retrieval-augmented generation)
- RAG is a technique that retrieves relevant documents from an external store and places them in the prompt, so the model answers from that material rather than from memory.
- Chunking
- Chunking is splitting documents into smaller passages before embedding them, so retrieval returns a relevant section rather than an entire file.
More on retrieval and agents
See the full glossary, read the guides, or put it into practice in the prompt builder.