@core / ai

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1.0.0

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Chat de IA con streaming, prompts versionados, tool calling y registro de uso y coste; modelo por env

Código11 archivosContexto~668 tokensescaneo superado

~668 tokens→ src/lib/ai/AGENTS.md→ .cursor/rules/genpm-core-ai.mdc

Esto es exactamente lo que lee tu IA cuando trabaja en src/lib/ai. No se añade nada más a su contexto.

@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.

El árbol exacto que se inyectará, tras aplicar .genpmignore. Anclado a

src/lib/ai/tools.tssolo lectura · 5b1e62b

// Scaffold de tool-calling. Cada tool: descripción clara para el modelo, `inputSchema` en zod y `execute` en el
// servidor. Valida y autoriza dentro de `execute`: el modelo decide cuándo llamarla, no si el usuario puede.
import { tool } from 'ai';
import { z } from 'zod';

export const tools = {
  currentTime: tool({
    description: 'Current date and time, optionally in an IANA time zone such as "Europe/Madrid".',
    inputSchema: z.object({ timeZone: z.string().optional() }),
    execute: async ({ timeZone }) => ({
      iso: new Date().toISOString(),
      local: new Date().toLocaleString('en-US', timeZone ? { timeZone } : {}),
    }),
  }),
};

export type AppTools = typeof tools;