@core / ai

1.0.0 ▾

1.0.0

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ストリーミング AI チャット：バージョン管理プロンプト、ツール呼び出し、使用量とコストの記録、モデルは環境変数で指定

コード11 ファイルコンテキスト約 668 トークンスキャン合格

約 668 トークン→ src/lib/ai/AGENTS.md→ .cursor/rules/genpm-core-ai.mdc

これは AI が src/lib/ai で作業するときに読む内容そのものです。それ以外はコンテキストに追加されません。

@core/ai — rules for AI agents

Purpose

Streaming chat endpoint on the Vercel AI SDK, with versioned system prompts, a tool-calling scaffold and per-response usage logging (tokens and cost) in the database. Provider and model come from env: nothing is hardcoded. No RAG, embeddings or chat history storage (store UIMessage[] yourself if you need it).

Map

index.ts — public API: streamChat, getModel, setModel, recordUsage, prompts, tools, PRICES.
prompts.ts — your config: system prompts with version. Bump version whenever you change a prompt.
tools.ts — your config: tools the model can call (zod inputSchema + server-side execute).
prices.ts — your config: USD per million tokens per AI_MODEL, for cost logging.
schema.ts — ai_usage table. Depends on ../db (@core/db).
adapters/hono.ts, adapters/next.ts — POST /ai/chat returning a UI message stream for useChat.

Integration

Requires Node 22+ (AI SDK 7). Env: AI_MODEL as <provider>:<model-id> plus AI_PROVIDER_API_KEY, and install that provider package: anthropic → @ai-sdk/anthropic, openai → @ai-sdk/openai, also google, mistral, groq, xai. Or AI_MODEL=<provider>/<model-id> with AI_GATEWAY_API_KEY (Vercel AI Gateway, no extra package). Ask the user which provider and model to use; do not pick one for them.
Generate and apply migrations (see src/lib/db/AGENTS.md).
Hono: app.route('/ai', aiRoutes({ getUserId: (c) => c.get('user')?.id, requireUser: true })) after @core/auth's sessionMiddleware (omit both options without auth). Next.js: app/api/ai/chat/route.ts → export const POST = chatRoute({ getUserId, requireUser: true }). Delete the adapter of the framework you don't use.
Frontend: useChat({ transport: new DefaultChatTransport({ api: '/ai/chat' }) }) from @ai-sdk/react.
Verify: curl -N -X POST localhost:3000/ai/chat -H 'content-type: application/json' -d '{"messages":[{"id":"1","role":"user","parts":[{"type":"text","text":"hi"}]}]}'.

Conventions

One prompt per use case in prompts.ts; pass it with streamChat({ prompt: 'assistant', … }).
Tools authorize inside execute using the user id; never trust ids sent by the model.
Query ai_usage for quotas (e.g. tokens per user per day) before calling streamChat.
Tests: setModel(new MockLanguageModelV4(...)) from ai/test.

Don't

Don't hardcode a model or API key, and don't call the provider from the browser.
Don't log prompts or completions with user data in production.
Don't let tools run destructive actions without an explicit user confirmation step.

# @core/ai — rules for AI agents

## Purpose
Streaming chat endpoint on the Vercel AI SDK, with versioned system prompts, a tool-calling scaffold and per-response
usage logging (tokens and cost) in the database. Provider and model come from env: nothing is hardcoded. No RAG,
embeddings or chat history storage (store `UIMessage[]` yourself if you need it).

## Map
- `index.ts` — public API: `streamChat`, `getModel`, `setModel`, `recordUsage`, `prompts`, `tools`, `PRICES`.
- `prompts.ts` — **your config**: system prompts with `version`. Bump `version` whenever you change a prompt.
- `tools.ts` — **your config**: tools the model can call (zod `inputSchema` + server-side `execute`).
- `prices.ts` — **your config**: USD per million tokens per `AI_MODEL`, for cost logging.
- `schema.ts` — `ai_usage` table. Depends on `../db` (@core/db).
- `adapters/hono.ts`, `adapters/next.ts` — `POST /ai/chat` returning a UI message stream for `useChat`.

## Integration
1. Requires Node 22+ (AI SDK 7). Env: `AI_MODEL` as `<provider>:<model-id>` plus `AI_PROVIDER_API_KEY`, and install
   that provider package: `anthropic` → `@ai-sdk/anthropic`, `openai` → `@ai-sdk/openai`, also `google`, `mistral`,
   `groq`, `xai`. Or `AI_MODEL=<provider>/<model-id>` with `AI_GATEWAY_API_KEY` (Vercel AI Gateway, no extra package).
   Ask the user which provider and model to use; do not pick one for them.
2. Generate and apply migrations (see `src/lib/db/AGENTS.md`).
3. Hono: `app.route('/ai', aiRoutes({ getUserId: (c) => c.get('user')?.id, requireUser: true }))` after
   `@core/auth`'s `sessionMiddleware` (omit both options without auth).
   Next.js: `app/api/ai/chat/route.ts` → `export const POST = chatRoute({ getUserId, requireUser: true })`.
   Delete the adapter of the framework you don't use.
4. Frontend: `useChat({ transport: new DefaultChatTransport({ api: '/ai/chat' }) })` from `@ai-sdk/react`.
5. Verify: `curl -N -X POST localhost:3000/ai/chat -H 'content-type: application/json' -d '{"messages":[{"id":"1","role":"user","parts":[{"type":"text","text":"hi"}]}]}'`.

## Conventions
- One prompt per use case in `prompts.ts`; pass it with `streamChat({ prompt: 'assistant', … })`.
- Tools authorize inside `execute` using the user id; never trust ids sent by the model.
- Query `ai_usage` for quotas (e.g. tokens per user per day) before calling `streamChat`.
- Tests: `setModel(new MockLanguageModelV4(...))` from `ai/test`.

## Don't
- Don't hardcode a model or API key, and don't call the provider from the browser.
- Don't log prompts or completions with user data in production.
- Don't let tools run destructive actions without an explicit user confirmation step.

.genpmignore 適用後に組み込まれる正確なツリーです。固定先:

src/lib/ai/adapters/next.ts読み取り専用 · 5b1e62b

// Adaptador Next.js (App Router): app/api/chat/route.ts → `export const POST = chatRoute();`
import type { UIMessage } from 'ai';
import { streamChat } from '../index.js';

export function chatRoute(opts: { getUserId?: (req: Request) => Promise<string | null | undefined>; requireUser?: boolean } = {}) {
  return async (req: Request): Promise<Response> => {
    const userId = (await opts.getUserId?.(req)) ?? null;
    if (opts.requireUser && !userId) return Response.json({ error: 'unauthorized' }, { status: 401 });
    const body = (await req.json().catch(() => ({}))) as { messages?: UIMessage[] };
    if (!Array.isArray(body.messages)) return Response.json({ error: 'messages required' }, { status: 400 });
    const result = await streamChat({ messages: body.messages, userId, abortSignal: req.signal });
    return result.toUIMessageStreamResponse();
  };
}