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implementation: Simple chat application using Moduler Prompt with MLX models","maintainers":[{"name":"otolab","email":"naoto.kato@plaid.co.jp"}],"readme":"# Simple Chat - サンプル実装\n\nModuler Promptフレームワークを使用したチャットアプリケーションのサンプル実装です。実際のアプリケーションでフレームワークをどのように使用するかを示すリファレンス実装として提供されています。\n\n## 概要\n\nこのパッケージは以下を実演します：\n- PromptModuleの静的定義とコンテキストバインディング\n- `merge`を使用したモジュール合成（`withMaterials`の活用）\n- MLXドライバーを使用したAIモデルとの対話\n- チャットログの管理とリソースファイルの処理\n\n## インストール\n\n```bash\nnpm install @modular-prompt/simple-chat\n```\n\n## 初回セットアップ（MLX）\n\nsimple-chat のデフォルトは MLX ローカル推論です。初回利用前に Python ランタイムの**明示的なセットアップ**が必要です（`npm install` では自動実行されません）。\n\nPython 環境はマシン共有の `~/.modular-prompt/runtimes/mlx/` に作成されます（プロジェクトや `node_modules` 内には作られません）。\n\n**前提条件**: macOS（Apple Silicon）、Python 3.13、uv（未インストール時はセットアップ中に自動インストール）\n\n### monorepo から利用する場合\n\n```bash\n# リポジトリルートから\npnpm run setup-mlx\n```\n\n公開パッケージとして利用する場合:\n\n```bash\nmodular-prompt-runtime setup mlx\n```\n\n### 状態確認\n\n```bash\n# simple-chat CLI（MLX ランタイム + ~/.modular-prompt/models.yaml の状態）\nsimple-chat --check\n\n# driver の runtime CLI\nmodular-prompt-runtime setup --status\n```\n\n`--check` は `MODULAR_PROMPT_HOME`（既定 `~/.modular-prompt`）、MLX venv の有無、`models.yaml` のパス・alias 一覧、マージ後の effective default を表示します。\n\n詳細は [@modular-prompt/driver README](https://github.com/otolab/modular-prompt/blob/main/packages/driver/README.md) および [ローカルモデルセットアップガイド](./docs/LOCAL_MODEL_SETUP.md) を参照してください。\n\n## 使用方法\n\n以下の CLI 例は、`~/.modular-prompt/models.yaml` に `models.default` を設定済みであることを前提にしています。最小構成は次のとおりです。モデルを設定しない場合は、各コマンドに `-m <model-id-or-alias>` を追加してください。\n\n```yaml\n# ~/.modular-prompt/models.yaml\nmodels:\n  default:\n    provider: mlx\n    model: mlx-community/gemma-3-270m-it-4bit\n```\n\n### CLIとして使用\n\n```bash\n# 直接メッセージを送信\nsimple-chat \"こんにちは\"\n\n# プロファイルを指定\nsimple-chat -p profile.yaml \"質問があります\"\n\n# チャットログを保存・継続\nsimple-chat -l chat.json \"会話を続けます\"\n\n# 標準入力から読み込み\necho \"長い質問文...\" | simple-chat --stdin\n\n# オプションの組み合わせ\nsimple-chat -p custom.yaml -l session.json --temperature 0.8 \"創造的な回答をお願いします\"\n\n# モデル指定を上書き\nsimple-chat -m local-chat \"こんにちは\"\nsimple-chat --provider pytorch -m my-model \"こんにちは\"\nsimple-chat --backend lm -m mlx-community/Qwen2-VL-2B-Instruct-4bit \"こんにちは\"\n\n# VLMモデルをtext-onlyモードで使用（--text-only は非推奨、--backend lm を推奨）\nsimple-chat --model mlx-community/Qwen2-VL-2B-Instruct-4bit --text-only \"こんにちは\"\n\n# VLMモデルで画像入力（Image-Text-to-Text）\nsimple-chat --model mlx-community/Qwen2-VL-2B-Instruct-4bit -i photo.jpg \"この画像について説明して\"\n\n# 複数画像を入力\nsimple-chat --model mlx-community/Qwen2-VL-2B-Instruct-4bit -i img1.jpg -i img2.jpg \"これらの画像を比較して\"\n```\n\n### ライブラリとして使用\n\n```typescript\nimport {\n  loadDefaultProfile,\n  loadDialogProfile,\n  createDriver,\n  performAIChat,\n  createChatLog,\n  addMessage,\n} from '@modular-prompt/simple-chat';\n\nconst profile = await loadDialogProfile('./profile.yaml');\nconst chatLog = createChatLog(profile);\n\naddMessage(chatLog, 'user', 'こんにちは');\n\nconst { response } = await performAIChat(profile, chatLog, 'こんにちは', {\n  modelOverrides: { model: 'local-chat' },\n});\n\n// またはドライバーを直接作成\nconst driver = await createDriver(profile, { model: 'local-chat' });\n```\n\n`performAIChat` の第4引数 `AIChatRunOptions` では `materials` / `modelOverrides` / `overrideDriver` を指定できます。CLI の `-m` / `--provider` / `--backend` は profile を書き換えず `modelOverrides` として渡されます。\n\n## モデル解決\n\nsimple-chat は **model 先行**でモデルを選びます。provider / MLX backend は model 決定後に上書き可能です。\n\n### 優先順位\n\n| 順位 | ソース | 例 |\n|------|--------|-----|\n| 1 | CLI override | `-m`, `--provider`, `--backend` |\n| 2 | `profile.model` | alias または生の model 名 |\n| 3 | `workflow.models.default` | `ref: local-chat` または `provider` + `model` |\n| 4 | マージ済み `models.default` | user yaml または profile `modelsConfig` overlay |\n\nいずれも未指定の場合はエラーです（暗黙の runtime / defaults 解決はありません）。\n\n### models.yaml との統合\n\nマシン共通のモデル定義は **`~/.modular-prompt/models.yaml`**（単一ファイル。`models/` ディレクトリは非対応）に置きます。`MODULAR_PROMPT_HOME` でホームディレクトリを変更できます。\n\nマージ優先（下ほど高）: **`~/.modular-prompt/models.yaml`** → **profile `modelsConfig` overlay**。simple-chat はモデルを同梱しないため、明示的な `-m`、`profile.model`、`workflow.models.default`、または user/profile の `models.default` が必要です。\n\n```yaml\n# ~/.modular-prompt/models.yaml の例\nmodels:\n  default:\n    provider: mlx\n    model: mlx-community/your-preferred-model\n  local-chat:\n    provider: mlx\n    model: mlx-community/your-preferred-model\n```\n\n`workflow.models.default.ref: local-chat` で alias を参照できます。**CLI `-m` / `profile.model` が最優先**で、上記を上書きします。\n\n生の model ID を `-m` または `profile.model` で指定した場合、provider は次の順で推論されます。\n\n1. merged `models.yaml` の `model` フィールド完全一致エントリの `provider`\n2. 一致エントリの `runtime` または `metadata.runtime`（`pytorch` / `pytorch-*` は `pytorch`、`mlx` / `mlx-lm` / `mlx-vlm` は `mlx`）\n3. モデル名の既知パターン（`mlx-community/...` や `-mlx-` など）\n\n`--provider` または `profile.provider` を指定した場合は、この推論結果を上書きします。上記から provider を推論できない生 model ID は、誤った runtime を起動しないようエラーになります。`--provider mlx` や `--provider pytorch` のように明示指定してください。\n\nsimple-chat は **デフォルトで `merge` モード**です。マシン共通の alias 定義（`local-chat` 等）を user yaml で共有しつつ、プロファイル overlay で上書きできます。\n\n- **`modelsConfig.mode: merge`**（既定）— user yaml をマージ\n- **`modelsConfig.mode: override`** — user yaml を無視し、profile overlay のみ\n\n> **#341 との方針**  \n> Issue #341 では user yaml の無視（`override` 固定）も検討されましたが、simple-chat では **マシン共通 alias の再利用**を優先し `merge` をデフォルトにしています。user yaml を使わない場合は profile で `modelsConfig.mode: override` を指定してください。\n\nローカル試行や統合テスト専用のモデルは `~/.modular-prompt/models.testing.yaml` に置けます。Vitest / `NODE_ENV=test` では自動マージされ、CLI では `MODULAR_PROMPT_MODELS_PROFILE=testing simple-chat -m default \"こんにちは\"` のように profile を明示します。セットアップ手順は [ローカルモデルセットアップガイド](./docs/LOCAL_MODEL_SETUP.md) を参照してください。\n\n### 内部構成（リファレンス実装）\n\n| 層 | 関数 | 責務 |\n|----|------|------|\n| マージ | `resolveMergedModels(profile)` | `AIService.fromMergedConfig` 経由で config 解決 |\n| 選択 | `resolveModelSpec(profile, models, overrides?)` | model 優先順位の適用（テスト向けに models 注入可） |\n| 生成 | `createAIService` → `createDriver` | `AIService.createDriver(spec)` でドライバー生成 |\n\n`resolveProfileModelSpec` は上記をまとめた統合 API（テスト・デバッグ用）です。\n\n\n対話プロファイルは、チャットの動作を制御するYAML形式の設定ファイルです。\n\n### プロファイルの構造\n\n```yaml\n# PromptModule の定義。systemPrompt ではなく module を使用します。\nmodule:\n  objective:\n    - チャットアシスタントとして、ユーザーの質問に回答する\n  instructions:\n    - 日本語で自然に応答する\n    - 不確実な情報は不確実であると明確に伝える\n\n# モデルは直接指定することも、models.yaml の alias を参照することもできます。\nworkflow:\n  mode: direct\n  models:\n    default:\n      ref: local-chat\n\n# ~/.modular-prompt/models.yaml への inline overlay（必要な場合のみ）\nmodelsConfig:\n  mode: merge\n  models:\n    local-chat:\n      provider: mlx\n      model: mlx-community/gemma-3-270m-it-qat-4bit\n\n# VLMモデルをtext-onlyモードで使用（オプション）\ntextOnly: false\n\n# 初回メッセージ（オプション）- 新規セッション開始時の挨拶\npreMessage: \"こんにちは！何かお手伝いできることはありますか？\"\n\n# 参照ファイル（オプション）- プロンプトに含める追加資料\nresourceFiles:\n  - \"./docs/guide.md\"\n  - \"./data/reference.txt\"\n\n# 生成オプション\noptions:\n  temperature: 0.7\n  maxTokens: 4000\n  topP: 0.9\n\n# KVキャッシュ（MLX専用、オプション）\ncacheDir: \".cache/mlx-kv\"\n\n# チャットログ（オプション）\nlogPath: \"./chat.log.json\"\n```\n\n### デフォルトプロファイル\n\n`-p` でプロファイルを指定しない場合、`loadDefaultProfile()` が次の設定をコードから生成します。リポジトリにあった `default-profile.yaml` は実行時には読み込みません。\n\n- **`module.objective`**: 最新のユーザーメッセージに対する返答を作成\n- **`module.instructions`**: 自然な日本語の対話とコンテキスト理解を重視\n- **`options.temperature`**: 1.0\n- **`options.maxTokens`**: 4000\n- **`options.topP`**: 0.95\n\nモデル解決の詳細は上記 [モデル解決](#モデル解決) を参照してください。`module` はチャット基盤の PromptModule と合成されるため、基盤側の日本語応答指示も含まれます。\n\n### プロファイルの活用例\n\n#### 1. 技術サポート用プロファイル\n\n```yaml\nmodule:\n  objective:\n    - ソフトウェア開発の専門家として技術的な質問に回答する\n  instructions:\n    - 具体的なコード例を交えて説明する\n    - エラーの解決方法を段階的に説明する\noptions:\n  temperature: 0.3\n```\n\n#### 2. VLMモデルで画像入力（Image-Text-to-Text）\n\n```yaml\nmodel: \"mlx-community/Qwen2-VL-2B-Instruct-4bit\"\nmodule:\n  objective:\n    - 画像の内容を分析し、ユーザーの質問に回答する\noptions:\n  temperature: 0.7\n  maxTokens: 4000\n```\n\nCLIで画像ファイルを指定して使用します：\n\n```bash\n# 単一画像\nsimple-chat -p vlm-profile.yaml -i photo.jpg \"この画像に何が写っていますか？\"\n\n# 複数画像\nsimple-chat -p vlm-profile.yaml -i before.jpg -i after.jpg \"変更点を教えて\"\n\n# チャットログで会話を継続（画像情報もログに保存されます）\nsimple-chat -p vlm-profile.yaml -l session.json -i diagram.png \"この図の説明をお願いします\"\n```\n\nVLMモデルは`config.json`の`model_type`から自動検出されます。画像は最大768pxにリサイズされて処理されます。\n\n#### 3. VLMモデルをテキストのみで使用\n\n```yaml\nmodel: \"mlx-community/Qwen2-VL-2B-Instruct-4bit\"\ntextOnly: true\nmodule:\n  objective:\n    - テキストによるユーザーの質問に回答する\noptions:\n  temperature: 0.7\n  maxTokens: 4000\n```\n\n#### 4. 創作支援用プロファイル\n\n```yaml\nmodule:\n  objective:\n    - 創造的な文章作成を支援する\n  instructions:\n    - ユーザーのアイデアを発展させる\n    - 独創的な提案を行う\noptions:\n  temperature: 1.2\n  maxTokens: 8000\n```\n\n### KVキャッシュの活用（MLX専用）\n\n`cacheDir`を指定すると、プロンプトの静的部分（システムプロンプト、会話履歴、資料など）のKV状態をファイルに保存し、2回目以降の推論を高速化できます。\n\n```yaml\ncacheDir: \".cache/mlx-kv\"  # キャッシュファイルの保存先\noptions:\n  temperature: 0.7\n  maxTokens: 4000\n```\n\n#### 特徴\n- MLXドライバー専用（他のプロバイダーでは無視されます）\n- 会話履歴や資料が多い場合に効果が大きい\n- キャッシュファイル（.safetensors.zip）は手動管理（`rm -rf <cacheDir>`でクリア）\n- ディレクトリが存在しない場合は自動作成される\n\n## 実装のポイント\n\nこのサンプル実装では、Moduler Promptフレームワークの主要な機能を実際のアプリケーションで活用する方法を示しています：\n\n1. **モジュール合成**: `buildChatModule` が基盤モジュールと profile `module` / `withMaterials` を `merge`\n2. **AIService 経由のドライバー生成**: `createDriver` が `AIService.fromMergedConfig` + 明示 `ModelSpec` で生成\n3. **CLI override の分離**: `ModelOverrides` で profile を書き換えず model / provider / backend を上書き\n4. **型安全なコンテキスト**: `ChatContext` による型定義と `compile` によるプロンプト生成\n\n詳細は[プロンプトモジュール仕様書](https://github.com/otolab/modular-prompt/blob/main/docs/PROMPT_MODULE_SPEC.md)の実装例セクションを参照してください。\n","readmeFilename":""}