---
description: Glossary to special Qualitative Cloud + VecDB API terminology
---

# Glossary

An example of a document in Relevance AI:

```
document_example = {
    "car": {
        "wheels":
            {
                "number": 4
            }
        }
    }
}
```

| Terminology              | Definition                                                                                         |
| ------------------------ | -------------------------------------------------------------------------------------------------- |
| Vectors                  | AKA embeddings, 1D arrays, latent space vectors                                                    |
| Models/Encoders          | Turns data into vectors (e.g. Word2Vec turns words into vectors)                                   |
| Vector Similarity Search | Nearest neighbor search, distance search                                                           |
| Dataset                  | Index, Table (a dataset is made up of multiple documents)                                          |
| Documents                | (AKA JSON, item, dictionary, row) - a document can contain vector and other important information. |
| Field                    | A field is the key to a Python dictionary.                                                         |
| Value                    | A value is the value of a Python dictionary                                                        |
