February 2025
Intermediate
376 pages
9h 25m
Japanese
Content preview from 仕組みからわかる大規模言語モデル 生成AI時代のソフトウェア開発入門
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5
（4.2.3参 照 ）。
次に、
ChatOpenAI
クラスを使って
ChatOpenAI
オブジェクトを作成します。
llm = ChatOpenAI()
ここではコンストラクタの引数を省略し、デフォルトの値を使用しています。
ChatOpenAI
クラスは、
OpenAI
の会話モデルに対応します。他のモデルを使う場合は、別のクラスを使う
必要があります。
最後に、会話モデルの
invoke
メソッドを使って、モデルに入力を与えて出力を取得します。
response = llm.invoke("あなたは何という言語モデルですか？")
print(response.content)
invoke
メソッドには、モデルに入力するメッセージを文字列で渡します。
次に出力を確認します。出力は
AIMessage
オブジェクトとして返されます。この結果、
OpenAI API を使っている場合は次のような出力が得られます。Anthropic やGoogle の会話モ
デルを使っている場合も、類似の結果が得られるはずです。
O U T
私はGPT-3という言語モデルです。
メッセージリストによる呼び出し（テキストのみ）
会話モデルには、チャット履歴を含むメッセージリストを渡すことができます。このメッ
セージリストには古いメッセージから新しいメッセージへと順番に格納されます。会話モデル
は、このチャット履歴に続く自然な次のメッセージを出力します。
リストに格納された各メッセージは、誰の発言によるものかが区別されている必要がありま ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.