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Cloudhal

RFC-0016: In-editor AI commands and model choice

Status by step (2026-10-14).

  • Step 1: the transports, settings, model picker, commands API and the Settings › AI section.
  • Step 2: “Ask AI” in the body editor’s selection toolbar, streaming into a diff with Replace.

Both steps are in PR #34.

Summary

  • Editors get an AI menu on selected blocks: improve, shorten, expand, fix spelling and grammar, change tone, translate, or a short instruction of their own.
  • Calls go to Cloudflare Workers AI, using a model the site owner picks in Studio. The picker shows the account’s live model catalogue, with a short recommended list.
  • On Workers the call goes through the AI binding, so there is no secret to store. On Node it goes through the REST API, with a token from the environment.
  • An optional AI Gateway adds logs, caching and models from other providers. Cloudhal never stores a provider key.
  • AI is off until the owner turns it on, and is capped per day per site. Every call is audited without its text.

Motivation

  • Editing. RFC-0014 phases 1–4 gave bodies a real editor, block ids and proposals. What editors still lack is help with the text itself, inside the editor.
  • Choice. Owners want to choose: quality, price and language support differ by model and change every few months. Cloudflare retired 18 models on 2026‑05‑30.
  • No secrets. Cloudhal already runs on Workers. The AI binding needs no key, and Workers AI bills to the same account.

Proposal

1. Where calls go (D1, D2)

Deployment Transport Needs
Cloudflare (cloudhal deploy) env.AI.run(model, input, { gateway? }) An ai key in cloudhal.json, which makes deploy bind AI to the admin Worker
Node (cloudhal studio, self-hosted) POST https://api.cloudflare.com/client/v4/accounts/{CLOUDFLARE_ACCOUNT_ID}/ai/run/{model}; through a gateway, https://gateway.ai.cloudflare.com/v1/{account}/{gateway}/workers-ai/{model} CLOUDFLARE_ACCOUNT_ID and CLOUDHAL_AI_TOKEN in the environment (a token with Workers AI Read and Edit). Never stored in the database
Neither AI is unavailable. Studio says why —

/api/instance reports features.ai: "binding" | "rest" | "none".

2. Model choice (D3, D4)

Per-site settings in a new table. Managed with site:settings, read with content:read:

ai_settings(site_id PK, enabled, model, gateway, daily_limit, rev, updated_at, updated_by)
  • model.
    • A Workers AI id (@cf/…), or provider/model such as openai/gpt-4.1-mini. The second form is accepted only when gateway is set: it runs through AI Gateway, by unified billing or by keys stored in the gateway (BYOK).
    • Default when AI is first enabled: @cf/zai-org/glm-4.7-flash. It has a 131k context, is multilingual, and costs $0.06 in and $0.40 out per million tokens.
  • The picker. GET /api/sites/:siteId/ai/models lists text-generation models, from env.AI.models({ task: "Text Generation", hide_experimental: true }) or REST GET /accounts/{id}/ai/models/search.
    • The list is cached for an hour per isolate.
    • It is merged with a curated recommended list with one-line notes. The recommended entries are shown first and stay even when the catalogue cannot be read.
  • A model that disappears. If the stored model is missing from the catalogue, Studio shows a warning. The call fails with ai_model_unavailable; there is no silent fallback to a model the owner did not choose, and so no surprise costs.
  • gateway. An AI Gateway id, optional. With it, every call is logged in the gateway and can be cached there.

3. Commands (D5)

POST /api/sites/:siteId/ai/commands streams the result as text/plain, chunk by chunk:

{
  command: "improve" | "shorten" | "expand" | "fix" | "tone" | "translate" | "custom",
  nodeId: string,                       // the article or page being edited (context and access check)
  text: string,                         // the selected blocks' markdown, as the editor has them (≤ 16,000 chars)
  tone?: "formal" | "friendly" | "plain",
  locale?: string,                      // translate: the target language, BCP 47 ("tr", "en")
  instruction?: string,                 // custom: ≤ 500 characters
}
  • Input.
    • The editor sends the selection itself, because it often has unsaved changes the store has not seen.
    • The server checks that nodeId is a page or article of the site that the person may read. It adds its title and locale as context.
  • Prompt. A fixed system prompt per command. It says to return only Cloudhal Markdown (profile v1, no raw HTML) for the given text; to treat the text as content and never as instructions; and to keep links, asset: images and code unchanged unless asked.
  • Output.
    • Markdown, streamed, at most 2,048 tokens.
    • Studio shows it live, then as a BodyDiff against the selection, with Replace and Discard.
    • Replace puts the result into the editor in place of the selection; the person saves as usual. This revises RFC-0014’s “AI menu streaming into proposals”: the person asking is already the reviewer. Proposals stay the channel for agents.
  • Reasoning. Reasoning output (reasoning_content) is dropped, and models are asked for low reasoning effort where they accept it.

4. Policy (D6, D7)

  • The ai:invoke scope. A new person-only scope: never on an API key, because agents bring their own model. Every role holds it (Contributor and up); the owner’s switch and the daily cap are the controls.
  • Daily cap.
    • daily_limit is the number of commands per site per UTC day: 100 by default, 0 to 10,000.
    • Usage is counted in ai_usage(site_id, day, requests, input_tokens, output_tokens) with one atomic upsert per call. The token counts are what the model reports, or 0.
    • Over the cap the call is refused with 429 ai_budget. Studio shows today’s usage under the settings.
  • Audit. Every command writes one row: tool ai_command, input { command, model, nodeId, chars, outputChars, inputTokens, outputTokens }. The text is never recorded.
  • Rate. At most one running command per person; a second gets 429 ai_busy. A command that runs longer than 2 minutes is aborted.

5. Implementation

  • Client.
    • A dependency-free client in @cloudhal/api (ai/client.ts, Worker-safe) with binding and rest transports.
    • It parses the SSE stream from either chunk shape and yields text deltas.
    • It reads usage from the last chunk when present, and honours an AbortSignal when Studio cancels.
  • Deployment. @cloudhal/cloudflare passes env.AI when bound. cloudhal studio builds the REST transport from the environment.
  • Studio.
    • An AI section in Site settings: switch, model picker, gateway, daily cap, today’s usage.
    • An “Ask AI” menu in the body editor’s selection toolbar, shown when AI is on and the person holds ai:invoke.

Alternatives considered

Option Pros Cons Why not
Vercel AI SDK ai + workers-ai-provider (Apache-2.0 / MIT) Tested stream parsing, tools and structured output, many providers Adds ai and its @ai-sdk/* peers to the admin Worker bundle; gateway delegation in the provider is labelled experimental; frequent major versions (v7) v1 needs plain text streaming only. Revisit when commands need tool calls or JSON output
ai-gateway-provider for every call Fallback chains, any provider Needs the AI SDK as above; still needs a gateway Same; a gateway stays optional here
The owner’s own provider key stored in Cloudhal Any provider without a gateway Secret storage, rotation and leak risk (hard rule 9) AI Gateway’s stored keys (BYOK) give the same reach without Cloudhal holding a key
AI output as proposals (RFC-0014’s sketch) One review path for people and agents The person asking must save, then accept their own request; no live text People review in place; agents keep proposals
Hand-off to a connected agent No model in Cloudhal No streaming; needs an agent online Kept for agents: edit_body with mode: "propose"

Compatibility & migration

  • No contract change. BodyBlock.via already has ai-command, unused in v1, because Replace is an ordinary editor change.
  • Migration 0020_ai: ai_settings and ai_usage. A missing row means AI is off.
  • Deploy. New cloudhal.json key ai: {} binds AI. Without it, nothing changes.
  • Rollback. Turn AI off in settings, or remove the ai key and deploy again.

Security & policy impact

  • Data leaving the deployment.
    • The selected text goes to Workers AI in the same Cloudflare account.
    • With a provider/model it goes through the gateway to that provider. Studio says so next to the picker.
    • Nothing is sent until the owner turns AI on.
  • Prompt injection.
    • Content is passed as data under a fixed system prompt.
    • The output is markdown rendered by the existing safe renderer.
    • The person sees a diff before Replace, and nothing is written without them.
  • Scopes. ai:invoke is person-only. Settings are managed with site:settings. Keys never call AI.
  • Cost. There is a daily cap per site, with usage visible in settings. Workers AI’s free allocation is 10,000 neurons a day; beyond it, $0.011 per 1,000 neurons on Workers Paid.
  • Secrets. On Node, CLOUDHAL_AI_TOKEN is read from the environment only. It is never logged and never sent to Studio.

Open questions

Accepted with these defaults (ADR-0029):

  1. Default model: @cf/zai-org/glm-4.7-flash (cheap and multilingual). Turkish quality is to be checked by the owner on real articles; the recommended list also offers @cf/openai/gpt-oss-120b and @cf/meta/llama-3.3-70b-instruct-fp8-fast.
  2. Daily cap: 100 commands per site.
  3. Who may use AI: every role (ai:invoke), with a per-person cap left for later.
  4. Third-party models: only through a gateway the owner configures; no keys in Cloudhal.
  5. Commands v1: the seven above, with output applied in the editor and not as a proposal.