nn-vs-ann

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Nearest Neighbors vs Approximate Nearest Neighbors

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# nn-vs-ann

Nearest Neighbors vs Approximate Nearest Neighbors

This repo runs a quick benchmark for calculating nearest neighbors for embeddings / representations / vectors / latent factors / whatever you want to call them now. This benchmark pits an exact nearest neighbors calculation using [numpy](https://numpy.org/) against an approximate nearest neighbors calculation using [hnswlib](https://github.com/nmslib/hnswlib). The main takeaway is that exact nearest neighbors calculations scale poorly, but it depends on what scale you need. For a million documents and 256-dimensional embeddings, the top 5 nearest neighbor embeddings can be found in ~50 ms.

## Benchmark

Time in seconds.

| num_embeddings   |     hnswlib |       numpy |
|:-----------------|------------:|------------:|
| 1,000            | 3.95775e-05 | 9.23061e-05 |
| 10,000           | 6.76441e-05 | 0.000368452 |
| 100,000          | 0.000103912 | 0.00536784  |
| 1,000,000        | 0.000141306 | 0.0546181   |
| 10,000,000       | 0.000205708 | 0.465007    |

![assets/results.png](https://raw.githubusercontent.com/EthanRosenthal/nn-vs-ann/main/assets/results.png)

System Details:

- Intel Core i7-7820X 3.6 GHz 8-core Processor (16 virtual cores, I think)
- 32 GB DDR4-3200 RAM.
- Ubuntu 22.04

## Usage

To run the benchmarks locally, clone this repo and then use [poetry](https://python-poetry.org/docs/) to install this package by running the following command in the root directory of this repo.

```commandline
poetry install 
```

Run the benchmarks by running

```commandline
python -m nn_vs_ann.benchmark
```

This will save a file to `/assets/results.csv`. You can generate the plot in this README by running

```commandline
python -m nn_vs_ann.viz
```

This will save a plot to `/assets/results.png`.

Lastly, you can update this here README with your benchmark results by running

```commandline
python -m nn_vs_ann.gen
```

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