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AppleScriptの穴

Useful & Practical AppleScript archive. Click '★Click Here to Open This Script' Link to download each AppleScript

ChatGPTで文章のベクトル化(Embedding)

Posted on 2月 26, 2023 by Takaaki Naganoya

OpenAIが提供しているREST APIを呼び出して、指定文章のベクトル化を行うAppleScriptです。実行のためにはOpenAIのWebサイトにサインアップして、実行のためのAPI Keyを取得してください。

ChatGPTなどのサービスを提供しているOpenAIにサインアップして、各種サービスをAppleScriptから利用できます。Freeアカウントでは1分あたりに発行できるクエリー数の上限が低めに設定されていますが、実験を行う程度であれば十分なレベルでしょう。

https://platform.openai.com/docs/introduction

「文章のベクトル化」は、大量の候補文を用意しておいてベクトル化し、新たにユーザーが与えた文章との「類似度」を計算するためのものです。

AppleScript名:Embedding(文章のベクトル化).scptd
—
–  Created by: Takaaki Naganoya
–  Created on: 2023/02/24
—
–  Copyright © 2023 Piyomaru Software, All Rights Reserved
—

use AppleScript version "2.4" — Yosemite (10.10) or later
use framework "Foundation"
use scripting additions

property NSString : a reference to current application’s NSString
property NSCountedSet : a reference to current application’s NSCountedSet
property NSJSONSerialization : a reference to current application’s NSJSONSerialization
property NSUTF8StringEncoding : a reference to current application’s NSUTF8StringEncoding

set myText to "今日はいい天気です。"

set barerKey to "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX"
set sRes to (do shell script "curl https://api.openai.com/v1/embeddings -H ’Content-Type: application/json’ -H ’Authorization: Bearer " & barerKey & "’ -d ’{\"input\": \"" & myText & "\", \"model\":\"text-embedding-ada-002\"}’")

set jsonString to NSString’s stringWithString:sRes
set jsonData to jsonString’s dataUsingEncoding:(NSUTF8StringEncoding)
set aRes to NSJSONSerialization’s JSONObjectWithData:jsonData options:0 |error|:(missing value)
set modelRes to aRes as {anything, record}
–> {object:"list", |data|:{{|index|:0, object:"embedding", embedding:{0.0024673557, 0.0037366515, -0.005702117, -0.025023261, 0.010659494, -0.0024349757, 0.0038791234, -0.031266123, 0.005248797, -0.023909388, -0.0052941293, 0.008710219, -0.0028316306, -0.019039437, -0.013780926, -0.009519719, 0.01660446, -0.020762052, 0.019881317, -0.025748573, 0.037897546, 0.025981707, -0.006569901, -0.01387159, -0.023209982, 0.0051743235, 0.014182438, -0.018974677, 0.031032987, -0.026655212, 0.029919116, -0.00975933, -0.007019983, 0.022381052, 0.010186747, -0.032120954, -0.023054557, 0.01536107, -0.0049476633, 0.005919063, 0.01542583, -0.019363238, 0.015335166, -0.0029741025, -0.02567086, 0.008626031, -0.008295755, 0.0018116607, -0.004814905, 0.028546205, 0.011533754, -0.006395049, -0.040876508, -0.038855996, -5.7595916E-4, 0.006106867, -0.014078822, -1.681736E-4, -0.005365365, -0.01260877, -0.0025499247, -0.013949302, -0.009280107, 0.01925962, 0.007266071, -0.01451919, -0.020917477, -0.0051937513, 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0.025981707, -0.006194293, -0.0065375213, 0.0109638665, -0.0057150694}}}, model:"text-embedding-ada-002-v2", usage:{total_tokens:9, prompt_tokens:9}}

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Posted in Natural Language Processing REST API Text | Tagged 12.0savvy 13.0savvy ChatGPT | 1 Comment

1 thoughts on “<span>ChatGPTで文章のベクトル化(Embedding)</span>”

  1. 11/23/23
    3:44 PM
    2023年11月23日
    3:44 PM

    Reply

    可変次元のベクトルに対応したコサイン類似度計算 – AppleScriptの穴 says:

    […] もともとは、ChatGPTのAPIを利用して自然言語テキストをベクトル化し、それらの類似度を計算させるための準備で用意しました。自分が試したときには、ChatGPTのREST APIは1536次元のベクト […]

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