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@iwaxse0430(iwax)in株式会社UK Holdings

無料：ターミナルからローカルAIと自然な会話をもとめて：LM Studio + Zsh 関数で簡単チャット環境を構築

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✅ できること

  • ターミナルを開いてローカル AI と直ぐにチャットできる
  • 文脈を維持しながらチャットができる
  • クラウドのサービスを使わずにローカルだけで完結する
  • （あれです。chat-gpt とか cursor のチャットをターミナルで且つローカルで実現したかった）

output.gif

✅ 前提

  • LMStudio がセットアップ済みであること・起動済み・API が使える状態
  • モデルは gemma-3-4b-it-qat を使用（レスの速さ重視で軽量を選択）
  • M1 Mac環境で動作確認済み

💻 how to use: 単発の質問（短文）

コマンドに続けてそのまま質問を入力することで、即座にAIからの回答を得られる

chat "curl コマンドで POST する方法は？"

💻 how to use: 複数行での入力（ヒアドキュメント形式）

シェルのヒアドキュメント (<< EOF) を使って、複数行のプロンプトを渡すことも可能。特にスクリプトや長文の質問を扱う場合に便利。

chat << EOF
jq コマンドで以下の JSON から email アドレスの一覧を取り出したい。

{
  "users": [
    { "name": "Alice", "email": "alice@example.com" },
    { "name": "Bob", "email": "bob@example.com" }
  ]
}
EOF

💻 導入方法

この関数を .zshrc に追加するだけ

vim ~/.zshrc

chat() {
  local model="gemma-3-4b-it-qat"
  if [ -z "$CHAT_HISTORY" ]; then
    CHAT_HISTORY='[{"role": "system", "content": "あなたは親切なアシスタントです。日本語で回答してくれます。"}]'
  fi
  if [ -t 0 ]; then
    input="$*"
  else
    input="$(cat)"
  fi
  CHAT_HISTORY=$(jq -c --arg msg "$input" '. + [{"role": "user", "content": $msg}]' <<< "$CHAT_HISTORY")
  curl -sN http://localhost:1234/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
      "model": "'"$model"'",
      "stream": true,
      "messages": '"$CHAT_HISTORY"'
    }' | while IFS= read -r line; do
      if [[ $line == data:\ * ]]; then
        json="${line#data: }"
        if [ $(echo "$line" | wc -l) -gt 1 ]; then
          json=$(echo "$json" | sed -e ':a' -e 'N' -e '$!ba' -e 's/\n/\\n/g')
          content=$(echo "$json" | jq -r '.choices[0].delta.content // empty' 2>/dev/null)
          content=$(echo "$content" | sed 's/\\n/\n/g')
          echo $content
        else
          content=$(echo "$json" | jq -r '.choices[0].delta.content // empty' 2>/dev/null)
          [ -n "$content" ] && printf "%s" "$content"
          buffer+="$content"
        fi
      fi
    done
  echo
  CHAT_HISTORY=$(jq -c --arg msg "$buffer" '. + [{"role": "assistant", "content": $msg}]' <<< "$CHAT_HISTORY")
}

ターミナルを開き直さない場合は忘れずに以下のコマンドを叩いて反映させる

source ~/.zshrc

💪 LM Studio の API を叩いてチャットする仕組み

LM Studio が提供するローカルエンドポイント http://localhost:1234/v1/chat/completions に対して、JSONを送信して返答を取得している。

💪 シンプルなAPI呼び出し（stream = false）

curl -sN http://localhost:1234/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
      "model": "gemma-3-4b-it-qat",
      "stream": false,
      "messages": [
        {"role": "system", "content": "あなたは親切なアシスタントです。日本語で回答してくれます。"},
        {"role": "user", "content": "あなたは誰ですか"}
      ]
    }'

{
  "id": "chatcmpl-4yv0trm70guibqj98zhi5m",
  "object": "chat.completion",
  "created": 1746188391,
  "model": "gemma-3-4b-it-qat",
  "choices": [
    {
      "index": 0,
      "logprobs": null,
      "finish_reason": "stop",
      "message": {
        "role": "assistant",
        "content": "私は、Googleによってトレーニングされた、大規模言語モデルです。人間のように会話したり、テキストを生成したりすることができます。 \n\n簡単に言うと、「AIアシスタント」としてあなたのお役に立てるように設計されています。何かお手伝いできることはありますか？ 😊\n"
      }
    }
  ],
  "usage": {
    "prompt_tokens": 28,
    "completion_tokens": 55,
    "total_tokens": 83
  },
  "stats": {},
  "system_fingerprint": "gemma-3-4b-it-qat"
}

💪 AIが喋ってる感を出す（stream = true）

curl -sN http://localhost:1234/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemma-3-4b-it-qat",
    "stream": true,
    "messages": [
      {"role": "system", "content": "あなたは親切なアシスタントです。日本語で回答してくれます。"},
      {"role": "user", "content": "あなたは誰ですか"}
    ]
  }' | while IFS= read -r line; do
    if [[ $line == data:\ * ]]; then
      echo $line
    fi
  done

data: {"id":"chatcmpl-94huqxugqh30xt7h4o6wl","object":"chat.completion.chunk","created":1746188646,"model":"gemma-3-4b-it-qat","system_fingerprint":"gemma-3-4b-it-qat","choices":[{"index":0,"delta":{"role":"assistant","content":"私は"},"logprobs":null,"finish_reason":null}]}
data: {"id":"chatcmpl-94huqxugqh30xt7h4o6wl","object":"chat.completion.chunk","created":1746188646,"model":"gemma-3-4b-it-qat","system_fingerprint":"gemma-3-4b-it-qat","choices":[{"index":0,"delta":{"role":"assistant","content":"、"},"logprobs":null,"finish_reason":null}]}
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"},"logprobs":null,"finish_reason":null}]}
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data: [DONE]

💪 チャットの文脈

LMStudio（OpenAI API）との対話履歴を messages に蓄積していくことで、会話のコンテキストを維持している。具体的には、プロンプト入力のたびに次のような処理を行っている。

messages.append({"role": "user", "content": prompt})
response = openai.ChatCompletion.create(
    model="gpt-4",
    messages=messages,
    ...
)
messages.append({"role": "assistant", "content": response.choices[0].message["content"]})

この履歴（messages）は、ターミナルが起動している間のみ保持される。つまり、1つのターミナルを「1セッション」として捉えており、そのセッション内では文脈を保持した対話が可能。一方、別のターミナルから新たに実行した場合は、履歴が初期化されるため、新たな文脈での対話となる。

🙇 おわりに

クラウドベースのサービスに比べて通信の可視性が高く、オンプレミス環境にも適している。例えばインターネット接続が制限された社内ネットワーク内でも、安全かつ柔軟に導入しやすい。加えて、ターミナルで完結する UI は、ちょっとしたコマンドの確認や man 代わりのリファレンスとしても期待できる。複雑なコマンドの構文やツールの使い方などを、わざわざブラウザを開くことなく、自然な対話形式で調べられるのは大きなメリットである。

「AIは万能ではない」ことを体験し、正解を期待しすぎないマインドセットを持つことが重要だと個人的には思う。命令の仕方ひとつでAIの出力が大きく変わることを理解すると良いと思う。AIとの信頼関係（笑）として対話力のメンタルも大事で、すぐ怒らず、冷静にAIの限界を理解しつつ“会話”できる力も必要だ💪

ここまで読んでくれてありがとうございました。

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