What is Embeddings and vector databases?
Lists of numbers that capture the meaning of text or images, stored so similar items can be found quickly.
In plain English
An embedding model turns a piece of content into a vector, a long list of numbers, so that items with similar meaning end up close together. A vector database stores these vectors and finds the nearest ones to a query. This is how search by meaning, rather than by exact words, is done.
Example
A search for “car won’t start” can find an article titled “Troubleshooting a dead battery” because their embeddings are close.
Where you will meet it
This idea belongs to the subject area of these practice tests. We do not publish or predict real exam questions.
- AWS AIP-C01 practice tests
- Databricks GenAI Engineer Associate practice tests
- NVIDIA NCA-GENL practice tests
- Google Cloud Generative AI Leader practice tests
Related terms
- Retrieval-augmented generation (RAG): Letting a model look up relevant documents first and answer using them.
- Large language model (LLM): A very large neural network trained on huge amounts of text to predict and generate language.
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