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@modular-prompt/driver\n\nAIモデルドライバーパッケージ - 様々なAIプロバイダーとの統一されたインターフェースを提供。\n\n## インストール\n\n```bash\nnpm install @modular-prompt/driver\n```\n\n## 基本的な使い方\n\n```typescript\nimport { compile } from '@modular-prompt/core';\nimport { OpenAIDriver } from '@modular-prompt/driver';\n\nconst driver = new OpenAIDriver({ model: 'gpt-4o-mini' });\nconst prompt = compile(myModule, context);\n\n// クエリ\nconst result = await driver.query(prompt, { temperature: 0.7 });\nconsole.log(result.content);\n\n// ストリーミング\nconst { stream, result: resultPromise } = await driver.streamQuery(prompt);\nfor await (const chunk of stream) {\n  process.stdout.write(chunk);\n}\n\nawait driver.close();\n```\n\n## エラーハンドリング\n\nすべてのドライバーは `QueryResult` を通じて統一されたエラー情報を提供します。\n\n```typescript\nconst result = await driver.query(prompt);\n\nif (result.finishReason === 'error') {\n  // errors フィールドでエラー詳細を確認\n  if (result.errors) {\n    for (const entry of result.errors) {\n      console.error(`[${entry.prefix}] ${entry.message}`);\n    }\n  }\n}\n\n// logEntries で全レベルのログを確認可能\nif (result.logEntries) {\n  console.log(`Query produced ${result.logEntries.length} log entries`);\n}\n```\n\n詳細は [Driver APIリファレンス](./docs/DRIVER_API.md) を参照してください。\n\n## 利用可能なドライバー\n\n| ドライバー | プロバイダー | 備考 |\n|-----------|------------|------|\n| `OpenAIDriver` | OpenAI | OpenAI互換API対応 |\n| `AnthropicDriver` | Anthropic | Claude（Vertex経由も可） |\n| `VertexAIDriver` | Google Cloud | Gemini + Model Garden（Qwen, Llama等） |\n| `GoogleGenAIDriver` | Google | APIキーのみで利用可能 |\n| `OllamaDriver` | Ollama | ローカルLLM |\n| `MlxDriver` | MLX | Apple Silicon専用（VLM対応） |\n| `PyTorchDriver` | PyTorch | Transformers + PyTorch（LIP、cpu-minimal 自動セットアップ） |\n| `VllmDriver` | vLLM | CUDA GPU推論（Linux） |\n| `TestDriver` | - | モックレスポンス |\n| `EchoDriver` | - | プロンプトをそのまま返す |\n\n各ドライバーの詳細な設定・オプションは `skills/driver-usage/SKILL.md` を参照。\n\n### PyTorchProcess の KV キャッシュ\n\n`PyTorchProcess` の低レベル API では、Transformers の text-only LM に対して、KV キャッシュを prefill して suffix を生成できます。`memory://` ref は同一 Python プロセス内だけで有効です。`cpu-minimal` backend で通常のファイルパスを指定すると、PyTorch backend 固有の `pytorch_kv_v1` 形式で KV と `.meta.json` を永続化できます。CUDA backend は process-local cache のみを使用し、cache ファイルを作成しません。\n\n`PyTorchDriver` に `PyTorchCacheController` を渡すと、`cache: true` のクエリでキャッシュ可能な instructions/data の prefix を自動的に prefill・再利用できます。固定の `cacheDir` を指定した controller がプロセス再起動後も cache を再利用できるのは `cpu-minimal` backend です。CUDA backend は process-local で、incremental/prefix metadata を拒否し、restart 後は miss になります。`QueryResult.usage` の `cacheReadTokens` / `cacheWriteTokens` には backend が報告した利用量を反映します。固定 `cacheDir` は runtime variant / device ごとに分け、CPU と CUDA で共有しないでください。現在の PyTorch backend では VLM・画像入力の cache は無効です。\n\n```typescript\nimport {\n  PyTorchCacheController,\n  PyTorchDriver,\n} from '@modular-prompt/driver';\n\nconst driver = new PyTorchDriver({\n  model: 'your-transformers-model',\n  cacheController: new PyTorchCacheController({\n    cacheDir: '/path/to/pytorch-cache',\n  }),\n});\n\nconst result = await driver.query(prompt, { cache: true });\nconsole.log(result.usage?.cacheReadTokens);\nconsole.log(result.usage?.cacheWriteTokens);\nawait driver.close();\n```\n\n```typescript\nimport { PyTorchProcess } from '@modular-prompt/driver';\nimport type { InferenceMessage } from '@modular-prompt/driver';\n\nconst process = new PyTorchProcess('your-transformers-model');\nconst prefixMessages: InferenceMessage[] = [\n  { role: 'system', content: 'You are a helpful assistant.' },\n  { role: 'user', content: 'Keep this conversation context.' },\n];\nconst queryMessages: InferenceMessage[] = [\n  ...prefixMessages,\n  { role: 'user', content: 'Answer the next question.' },\n];\n\ntry {\n  const cache = await process.cachePrefill('memory://conversation-prefix', prefixMessages);\n  const rendered = await process.render(queryMessages);\n  if (rendered.error || rendered.formatted_prompt == null) {\n    throw new Error(rendered.error ?? 'Failed to render the prompt');\n  }\n\n  const stream = await process.generate(\n    rendered.formatted_prompt,\n    { max_tokens: 64, temperature: 0 },\n    undefined,\n    undefined,\n    cache.cache_path,\n  );\n  for await (const chunk of stream) {\n    process.stdout.write(chunk.toString());\n  }\n} finally {\n  await process.exit();\n}\n```\n\n`cachePrefill()` の結果には prefill した `token_count` と `cache_write_tokens` が含まれ、同じ参照を最初に使う `generate()` の LIP meta にも write 数が一度だけ通知されます。キャッシュを使った生成では `cache_read_tokens` と `cache_write_tokens`、実際に参照を読み込めたかどうかは `cache_loaded` で確認できます。\n\nprefill に渡した prompt の token 列は、`generate()` に渡す rendered prompt の先頭と一致している必要があります。`memory://` ref は `PyTorchProcess` または Python 子プロセスの終了・再起動で失われます。`cpu-minimal` のファイル cache は同じ model ID・dtype・device の backend から再利用できますが、CUDA はファイル cache を作成せず restart 後に miss になります。不一致や破損したファイルは cache miss として full prompt の cold path にフォールバックします。`baseCachePath` と `trimToTokens` を指定した `cachePrefill()` では、`cpu-minimal` で base cache を trim して suffix だけを prefill できます。CUDA は incremental prefill をサポートしません。`cacheTrimTokens` を指定した `generate()` の trim は生成用 clone に適用され、同じ cache ref の元 state は保持されます。`PyTorchCacheController` はこの低レベル API を `PromptCacheController` のライフサイクルに接続し、同一 query の prefill 増分を `cacheWriteTokens` として報告します。\n\n## 主な機能\n\n- **統一インターフェース**: `query()` / `streamQuery()` / `close()` の3メソッド\n- **推論キャンセル**: `QueryOptions.signal`（`AbortSignal`）— 現時点は MLX ドライバーで実装\n- **トークン使用量**: `QueryResult.usage`（`cacheReadTokens` / `cacheWriteTokens` 含む）\n- **ツール呼び出し**: Function Calling対応（OpenAI、Anthropic、VertexAI、GoogleGenAI）\n- **構造化出力**: JSONスキーマによる出力制御\n- **AIService**: 能力ベースのモデル自動選択\n\n## ユーザーモデル設定（`~/.modular-prompt/models.yaml`）\n\nマシン共通のモデル定義を `~/.modular-prompt/models.yaml` に置けます（`MODULAR_PROMPT_HOME` で上書き可）。**プロジェクト配下の暗黙探索は行いません。** 利用側アプリが base / overlay を明示投入し、**overlay > user > base** の優先順位で解決します。\n\n```yaml\nmodels:\n  default:\n    provider: mlx\n    model: mlx-community/gemma-3-270m-it-4bit\n  local-chat:\n    provider: mlx\n    model: mlx-community/gemma-3-270m-it-4bit\n    capabilities: [local, chat, tools]\n```\n\n```typescript\nimport {\n  resolveModelName,\n  resolveDefaultModelFromConfig,\n  AIService,\n} from '@modular-prompt/driver';\n\nconst bundled = {\n  models: {\n    default: { provider: 'mlx', model: 'app/default' },\n  },\n} as const;\n\nconst profileOverlay = {\n  models: {\n    'local-chat': { provider: 'mlx', model: 'my-app/local' },\n  },\n};\n\nconst ai = AIService.fromMergedConfig(bundled, profileOverlay);\nconst models = ai.modelsConfig;\n\nconst spec = resolveModelName('local-chat', models);\nconst fallback = resolveDefaultModelFromConfig(models);\nconst driver = await ai.createDriver(spec);\n```\n\n- `AIService.fromMergedConfig(base, overlay)` — base + overlay をマージし、デフォルトで user yaml も取り込む\n- `AIService.fromModelsConfig({ source })` — `source: 'merge'`（デフォルト）で user yaml を読み込み、`source: 'overlay'` で無視\n- `mode: 'merge' | 'override'` — models セクションの浅いマージ / 置換\n- デフォルト model は `models.default` alias、なければ models の先頭エントリから導出\n- `defaults` によるモデルの暗黙解決は廃止（YAML に残っていても警告のみ）。`runtime` はモデル決定後の provider 推論に限って利用する\n- 生 model ID は `resolveModelName()` が merged models の model 完全一致エントリ、`runtime` / `metadata.runtime`、既知のモデル名パターンの順で provider を推論する。推論できない場合は `--provider <provider>` などで明示指定する\n\nsimple-chat では profile の `modelsConfig` に inline の `models` / `drivers` を載せ、`workflow.models.default.ref` で alias 参照できます。`ref` に未知の alias を指定した場合は **エラーで停止**します。\n\n不正な YAML は `loadModelsConfigFile()` が **例外を throw** します（js-yaml のパースエラーをそのまま伝播）。\n\n### テスト用モデル設定（`models.testing.yaml`）\n\nローカル統合テストや手元試行で使うモデルは、通常設定と分けて\n`~/.modular-prompt/models.testing.yaml` に置けます。`MODULAR_PROMPT_HOME` を設定している場合は、そのディレクトリ配下を使用します。プロジェクト配下の設定ファイルは暗黙探索しません。\n\n`models.default` には cache 対応の text-only LM または cache を使う MLX VLM を指定できます。MLX VLM の cache は `backend: vlm`（または `auto`）で起動し、画像なしリクエストは `mlx-vlm==0.7.0` の `exact_cache_v1`、画像付きリクエストは専用の `vision_cache_v1` namespace としてディスクへ保存されます。画像付き cache は text-only VLM / LM の cache（LM の `.safetensors.zip` を含む）とは相互利用できません。\n\nMLX VLM の画像入力は local file path のみ対応します。URL / data URI の loader は実装していないため、画像付き cache 経路でも利用できません。\n\n```yaml\nmodels:\n  default:\n    provider: mlx\n    model: prism-ml/Ternary-Bonsai-1.7B-mlx-2bit\n    driverOptions:\n      backend: lm  # qwen3 is otherwise detected through mlx_vlm; keep cache on\n  mlx-native-tool:\n    provider: mlx\n    model: mlx-community/Josiefied-LFM2.5-1.2B-Instruct-abliterated-4bit\n    capabilities: [tools]\n```\n\nマージ順は **base → `models.yaml` → `models.testing.yaml` → overlay** です。Vitest 実行中または `NODE_ENV=test` では testing 設定が自動で追加されます。CLI などで明示的に使う場合は次の環境変数を指定します。\n\n```bash\nMODULAR_PROMPT_MODELS_PROFILE=testing modular-prompt-extract create meeting -m default docs/notes.txt\n```\n\nライブラリからは `resolveModelsConfig({ profile: 'testing' })` または `AIService.fromModelsConfig({ profile: 'testing' })` を使用できます。統合テストはこの設定から MLX/API ドライバーを導出し、設定が無い場合は従来の `test-drivers.yaml` にフォールバックします。サンプルは [models.testing.yaml.example](https://github.com/otolab/modular-prompt/blob/main/packages/driver/test/integration/models.testing.yaml.example) または同梱 [LOCAL_MODEL_SETUP.md](./docs/LOCAL_MODEL_SETUP.md) を参照してください。\n\n\n```typescript\nimport { MlxDriver } from '@modular-prompt/driver';\n\nconst driver = new MlxDriver({ model: 'mlx-community/...' });\nconst controller = new AbortController();\n\nconst { stream, result } = await driver.streamQuery(prompt, {\n  signal: controller.signal,\n});\n\nfor await (const chunk of stream) {\n  process.stdout.write(chunk);\n}\n\n// 途中でキャンセルする場合\ncontroller.abort();\n\nconst final = await result;\n// final.finishReason === 'error' かつ controller.signal.aborted → キャンセル\n```\n\n- `result` はキャンセル時も **reject しません**（`finishReason: 'error'` で resolve）\n- キャンセル前の部分応答は `result.content` に残ります\n- 他ドライバーは `signal` を無視します（未実装）\n\n詳細は [Driver APIリファレンス](./docs/DRIVER_API.md#推論キャンセルabortsignal) を参照。\n\n## トークン使用量（usage）\n\n```typescript\nconst { stream, result } = await driver.streamQuery(prompt, { cache: true });\nfor await (const _ of stream) { /* consume */ }\n\nconst final = await result;\nif (final.usage) {\n  console.log(final.usage.promptTokens, final.usage.completionTokens);\n  console.log(final.usage.cacheReadTokens);   // MLX/PyTorch + KV キャッシュ時\n  console.log(final.usage.cacheWriteTokens);  // 同一クエリ内の新規 prefill 時\n}\n```\n\nusage は `stream` の各チャンクではなく **`result.usage` のみ** に載ります。\n\n## 共通ユーティリティ（query-utils）\n\nカスタムドライバーやアダプタ向けヘルパー:\n\n```typescript\nimport {\n  buildQueryUsage,\n  createAbortedStreamResult,\n  isAborted,\n  watchAbortSignal,\n} from '@modular-prompt/driver';\n```\n\n## カスタムドライバーの作成\n\n`AIDriver` インターフェースを実装:\n\n```typescript\nimport type { AIDriver, CompiledPrompt, QueryOptions, QueryResult, StreamResult } from '@modular-prompt/driver';\n\nexport class CustomDriver implements AIDriver {\n  async query(prompt: CompiledPrompt, options?: QueryOptions): Promise<QueryResult> {\n    // 実装\n  }\n\n  async streamQuery(prompt: CompiledPrompt, options?: QueryOptions): Promise<StreamResult> {\n    // 実装\n  }\n\n  async close(): Promise<void> {\n    // リソースのクリーンアップ\n  }\n}\n```\n\n## ローカルモデルのセットアップ\n\n### MLX（Apple Silicon）\n\nMLX ドライバーを使う前に Python ランタイムのセットアップが必要です（macOS のみ）:\n\n```bash\n# monorepo ルートから\npnpm run setup-mlx\n\n# @modular-prompt/driver を npm インストールした場合\nmodular-prompt-runtime setup mlx\n```\n\nランタイムは `~/.modular-prompt/runtimes/mlx/` に作成されます。状態確認は `pnpm --filter @modular-prompt/driver run runtime:status`（monorepo）または `modular-prompt-runtime setup --status`。\n\n前提条件: Python 3.13、Apple Silicon Mac、uv。\n\n#### VLMモデルのtext-only使用\n\nVLM（Vision Language Model）対応モデルを画像なしのテキストのみで使用する場合は、`textOnly`オプションを使用します。\n\n```typescript\nimport { MlxDriver, MlxCacheController } from '@modular-prompt/driver';\n\nconst driver = new MlxDriver({\n  model: 'mlx-community/Qwen2-VL-2B-Instruct-4bit',\n  textOnly: true,  // VLMモデルをtext-onlyモードで起動\n  defaultOptions: {\n    temperature: 0.7,\n    maxTokens: 500\n  }\n});\n\nconst result = await driver.query(prompt);\nconsole.log(result.content);\n\nawait driver.close();\n```\n\n`textOnly: true`を指定すると、VLM対応モデルを`mlx-lm`（高速起動）で起動し、画像処理なしのテキストのみで使用できます。\n\n`textOnly: true` は互換性のために VLM モデルを LM backend として起動する指定です。VLM backend 自体を使用して text-only cache を永続化する場合は、`backend: 'vlm'`（省略時の `auto` でも可）を指定し、cache controller を共有します。\n\n```typescript\nconst cacheController = new MlxCacheController({\n  cacheDir: '/path/to/cache',\n});\nconst driver = new MlxDriver({\n  model: 'mlx-community/Qwen2-VL-2B-Instruct-4bit',\n  backend: 'vlm',\n  cacheController,\n});\n```\n\nVLM の text-only cache は `cacheDir/<key>.vlm.safetensors/exact_<hash>.safetensors`、画像付き cache は `cacheDir/<key>.vlm-vision.safetensors/exact_<hash>.safetensors` に保存されます。実体ごとの `.meta.json` に `token_count` 等を記録し、画像付き sidecar には image hash、resize 条件、`vision_cache_v1` の version も記録します。VLM の incremental prefill と LM archive との互換性は提供しません。画像 feature tensor 自体は process-local の `VisionFeatureCache` に保持し、再起動後は再構築します。Pinned `mlx-vlm==0.7.0` の PIL key は bytes のみですが、MLX backend は mode・寸法・bytes・画像列を含む wrapper key を upstream cache に渡して衝突を防ぎます。\n\n#### 特殊トークンの確認\n\nモデルがサポートする特殊トークンを確認できます:\n\n```bash\nnpx tsx scripts/check-special-tokens.ts <model-name>\n# 例:\nnpx tsx scripts/check-special-tokens.ts mlx-community/gemma-3-270m-it-qat-8bit\n```\n\n#### Thinking系モデルの推論深度制御\n\n`reasoningEffort` オプションで推論深度を制御できます（llm-jp-4-thinking等に対応）:\n\n```typescript\nconst result = await driver.query(prompt, {\n  reasoningEffort: 'high',  // 'low' | 'medium' | 'high'\n  temperature: 0.7\n});\n\n// thinking チャネルの内容を取得\nif (result.thinkingContent) {\n  console.log('Thinking:', result.thinkingContent);\n}\nconsole.log('Response:', result.content);\n```\n\n**対応モデル**:\n- `llm-jp-4-*-thinking` シリーズ（Harmonyフォーマット）\n- `reasoningEffort` は Python側の `apply_chat_template` に渡されます\n\n**Harmonyフォーマット**: llm-jp-4が採用するOpenAI Harmony Response Format。スペシャルトークン（`<|start|>`, `<|channel|>`, `<|message|>` 等）でメッセージを分割し、`analysis` チャネルを `thinkingContent`、`final` チャネルを `content` に振り分けます。\n\n**技術詳細**: Harmonyフォーマットの後処理設計、ResponseProcessorアーキテクチャ、tool call形式については [prompts/memos/harmony-format-postprocessing.v1.md](../../prompts/memos/harmony-format-postprocessing.v1.md) を参照してください。\n\n### vLLM（CUDA GPU）\n\nvLLMドライバーは独立したPythonエンジンプロセスとして起動します。\n\n```bash\n# 環境のセットアップ\ncd node_modules/@modular-prompt/driver/src/vllm/python\nuv sync\n\n# エンジンの起動\nuv --project . run python __main__.py \\\n  --model Qwen/Qwen2.5-7B-Instruct \\\n  --socket /tmp/vllm.sock \\\n  --tool-call-parser hermes\n```\n\n前提条件: Python 3.10以上、CUDA対応GPU、Linux。\n\n詳細は [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## Skills（Claude Code向け）\n\nこのパッケージには `skills/driver-usage/SKILL.md` が同梱されています。Claude Codeのスキルとして利用でき、ドライバーの使い方をガイドします。\n\n## ライセンス\n\nMIT\n","readmeFilename":""}