OpenAIのEmbeddingsを実行してみた


## 概要


OpenAIの[Embeggings API](https://platform.openai.com/docs/api-reference/embeddings)を使用して、テキストの類似度を求めてみました。

この仕組みを利用することで、全文検索やクラスタリングなど様々の応用ができます。

## Embeddingとはどのようなことを可能にするのか？

公式ドキュメントが一番わかりやすいので、こちら読むことをおすすめします！

https://platform.openai.com/docs/guides/embeddings/what-are-embeddings

ざっくりと、下記のようなものだと理解しています。
 - 文章を検索しやすいようにベクトル化できる
 - 近いベクトル同士を類似度という数値で出力してくれる


# 実践

## 前提

OpenAI APIをPythonで実行できる環境が準備されていること

https://qiita.com/yamii/items/914fcae6599a4ac9f9fd

## Embedding APIの実行

ものすごく簡単なコードでAPIを実行してみます！

```test.py
import openai

openai.api_key = '[API KEY]'

INPUT = 'テスト'

# 検索用の文字列をベクトル化
query = openai.Embedding.create(
    model='text-embedding-ada-002',
    input=INPUT
)

print(query['data'][0])


```

長いのでアコーディオンに収めますが、このような形でベクトル化されたものが返却されます。


<details><summary>実行結果</summary><div>

```python
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  "index": 0,
  "object": "embedding"
}

Process finished with exit code 0

```　
</div></details>


## まとめ

実行結果をエディタに貼り付けたところ、Qiitaエディタが激重になってしまったのでこの記事は一旦実行してみる部分のみで公開します。
次の記事で、テキスト類似度を計測していくのでお楽しみに！