> ## Documentation Index
> Fetch the complete documentation index at: https://docs.vectoraidb.actian.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Points overview

> Points are the fundamental data units in Actian VectorAI DB, each containing a unique ID, a vector embedding, and an optional JSON payload. Covers point structure, supported operations, and how points relate to collections.

*Points* are the fundamental data units in Actian VectorAI DB. Each point contains the following three components:

* A unique ID
* A [vector embedding](/docs/fundamentals/vectors/vectors)
* An optional metadata [payload](/docs/fundamentals/payload/payload)

$$
\text{Point} = \text{ID} + \text{Vector} + \text{Payload}
$$

Points represent any data type, including text, images, audio, video, and structured data. Unlike traditional database rows with fixed schemas, VectorAI DB points store high-dimensional embeddings with rich metadata through flexible JSON payloads.

## Point structure

Each point contains three key components:

```json theme={null}
{
  "id": 1,                            // Required: Unique integer identifier
  "vector": [0.12, -0.34, 0.56, ...], // Required: Fixed-size embedding
  "payload": {                        // Optional: JSON metadata
    "name": "Laptop",
    "category": "electronics",
    "price": 999.99,
    "in_stock": true
  }
}
```

The following table describes each component:

| Component | Required | Description                                                                |
| --------- | -------- | -------------------------------------------------------------------------- |
| ID        | Yes      | Unique integer identifier within the collection.                           |
| Vector    | Yes      | Fixed-size embedding that matches your collection dimension configuration. |
| Payload   | No       | Nested JSON for rich filtering and metadata storage.                       |

## Point operations overview

VectorAI DB supports the following operations for managing points:

| Operation | Description                                                                                                                      |
| --------- | -------------------------------------------------------------------------------------------------------------------------------- |
| Insert    | Add individual points or batches of points to your collection.                                                                   |
| Retrieve  | Fetch specific points from your collection by their IDs.                                                                         |
| Update    | Modify existing points by providing new vector and payload data.                                                                 |
| Delete    | Remove individual points or batches of points from your collection.                                                              |
| Compact   | Reclaim storage space and optimize the index after deletions. See [Delete points](/docs/fundamentals/points/delete-points-task). |

## Task guides

See step-by-step guides for point operations:

* [Insert points](/docs/fundamentals/points/insert-points-task)
* [Retrieve points](/docs/fundamentals/points/retrieve-points-task)
* [Update points](/docs/fundamentals/points/update-points-task)
* [Delete points](/docs/fundamentals/points/delete-points-task)

## Next steps

* [Payload management](/docs/fundamentals/payload/payload) — Attach and filter metadata on points.
* [Vector embeddings](/docs/fundamentals/vectors/vectors) — Understand the embeddings stored in points.
* [Search operations](/docs/fundamentals/search/search) — Run similarity queries across points.
* [Filtering](/docs/fundamentals/filtering/filtering) — Narrow search results using point payload fields.