> For the complete documentation index, see [llms.txt](https://docs.envector.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.envector.io/1.4.x/sdk-user-guide/advanced-user-guide/ann.md).

# ANN

Approximate Nearest Neighbor (ANN) indexing trades a small amount of recall for large performance gains on similarity search. enVector supports three index types — one exact baseline and two IVF-based ANN variants:

| Index type | When to use                                                                                                       |
| ---------- | ----------------------------------------------------------------------------------------------------------------- |
| `FLAT`     | Default. Exact brute-force scan — 100% recall, slowest. Best for small datasets or when recall must be exact.     |
| `IVF_FLAT` | General-purpose ANN. Train centroids on your data with KMeans, or let the client/server initialize them randomly. |
| `IVF_VCT`  | Largest datasets where IVF\_FLAT is slow. Requires centroids pre-trained on the dataset.                          |

See:

* [IVF\_FLAT](/1.4.x/sdk-user-guide/advanced-user-guide/ann/ann-ivf-flat.md)
* [IVF\_VCT](/1.4.x/sdk-user-guide/advanced-user-guide/ann/ann-ivf-vct.md)


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