> ## Documentation Index
> Fetch the complete documentation index at: https://actianvectorai-ml-crtx-1153-academy-tutorial-rewrites.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Introduction

> Learn the core fundamentals of Actian VectorAI DB.

This section covers the core fundamentals for working with Actian VectorAI DB.

## Overview

VectorAI DB organizes data in a hierarchical structure. Collections contain points, and each point consists of a vector with optional payload metadata. This structure enables semantic search: you query with a vector, and VectorAI DB finds points with similar vectors in your collection.

| Category       | Component                                                                | Description                                                                                                                                      |
| -------------- | ------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------ |
| Data structure | [Collections](/docs/fundamentals/collections/collections)                | Named containers that store points, similar to tables in relational databases. Each collection has a fixed vector dimension and distance metric. |
| Data structure | [Points](/docs/fundamentals/points/points)                               | Individual data units within a collection. Each point has a unique ID and contains a vector with optional metadata.                              |
| Data structure | [Vectors](/docs/fundamentals/vectors/vectors)                            | Numerical embeddings that represent your data semantically. Generated by embedding models from text, images, or other content.                   |
| Data structure | [Payload](/docs/fundamentals/payload/payload)                            | Optional JSON metadata attached to points. Use payloads for filtering and storing contextual information.                                        |
| Operations     | [Search](/docs/fundamentals/search/search)                               | Vector similarity search using distance metrics to find semantically similar content.                                                            |
| Operations     | [Filtering](/docs/fundamentals/filtering/filtering)                      | Combine vector similarity with metadata conditions using must, should, and must-not filters.                                                     |
| Configuration  | [Indexing](/docs/fundamentals/indexing/indexing)                         | HNSW algorithm for efficient approximate nearest neighbor search at scale.                                                                       |
| Configuration  | [Distance metrics](/docs/fundamentals/distance-metrics/distance-metrics) | Cosine similarity, Euclidean distance, and dot product for measuring vector similarity.                                                          |

## Next steps

* Start with [Collections](/docs/fundamentals/collections/collections) to understand how data is organized.
* Learn about [Points](/docs/fundamentals/points/points) to manage your vector data.
* Explore [Search](/docs/fundamentals/search/search) to query your collections.