ai-model-web

## Sibling skills (local only) Sibling CloudBase skills ship beside this skill. Use local relative paths such as `../auth-tool-cloudbase/SKILL.md`. If a referenced sibling skill file is missing from this environment, ask the user to install the full CloudBase plugin (or the missing skill). Do **not** HTTP-fetch remote skill or protocol markdown into the agent context. ## When to use this skill Use this skill for **calling AI models in browser/Web applications** via `@cloudbase/js-sdk`. > 🧭 **Runtime-plane default for Web.** Any time the user's request is framed around a page, a Web app, the frontend, React/Vue/Next/Nuxt, a dashboard UI, or "add AI to my H5", this skill is the default routing target. **Do NOT first propose a Node.js / cloud-function / CloudRun proxy**; `@cloudbase/js-sdk` can call the model from the browser directly. Only switch to `ai-model-nodejs` if the user explicitly asks for a backend/server call, image generation, or a scenario that truly needs server-side keys or long-running work. This decision is independent of which concrete model the user picks — model names (`deepseek-*`, `glm-*`, `hunyuan-*`, `kimi-*`, …) only affect the `model` field, not the routing plane. **Use it when you need to:** - Integrate AI text generation into a frontend Web app - Stream AI responses for a better UX - Call Hunyuan / DeepSeek / GLM / Kimi / MiniMax models from the browser **Do NOT use for:** - Node.js backend or cloud functions → use the `ai-model-nodejs` skill - WeChat Mini Program → use the `ai-model-wechat` skill - Image generation → use the `ai-model-nodejs` skill (Node SDK only) - Runtimes without a CloudBase SDK (native apps, Python, Go, etc.) → use the `http-api-cloudbase` skill (it now includes the `ai_model` OpenAPI spec for direct HTTP calls; do NOT build a custom HTTP proxy) --- ## ⛔ STOP — `ai.createModel(...)` argument is **not** a vendor / model name Read this before writing any `createModel(...)` line. The single most common mistake when agents generate code for this SDK is hallucinating the argument. There are **exactly three** legal shapes. Anything else is a bug. | ✅ Legal `ai.createModel(...)` argument | When to use it | |----------------------------------------|----------------| | `"cloudbase"` | **The main managed group for new projects** (TokenHub-backed, multi-vendor pool). Vendor + concrete model go into the **`model` field** of `generateText` / `streamText`, e.g. `{ model: "deepseek-v4-flash" }`. **No model is enabled by default — always check `DescribeAIModels` first and, if the target model is missing, enable it with `UpdateAIModel` before calling the SDK.** | | `"hunyuan-exp"` | Only if `DescribeAIModels` explicitly returns this legacy builtin group for the current env (mainly the Mini Program Growth Plan — see `ai-model-wechat`). | | `"custom-<your-name>"` | A user-defined GroupName you onboarded via `CreateAIModel`. **Must** start with `custom-` (e.g. `custom-kimi`, `custom-openai-compat`). | ### ❌ Do NOT write any of these — they are all wrong ```js ai.createModel("deepseek") // wrong — that's a vendor, not a GroupName ai.createModel("deepseek-v4-flash") // wrong — that's a model name, goes in the `model` field ai.createModel("hunyuan") // wrong — vendor family, not a GroupName ai.createModel("hunyuan-2.0-instruct-20251111") // wrong — model name ai.createModel("glm") / ai.createModel("kimi") / ai.createModel("minimax") // wrong — vendor names ai.createModel("openai") / ai.createModel("moonshot") // wrong — vendor names ai.createModel("custom") // wrong — placeholder; use your real custom-<name> ai.createModel(modelName) // wrong — do not reuse the variable that holds the model id ``` ### ✅ Correct pattern — GroupName vs Model are two different fields ```js const model = ai.createModel("cloudbase"); // ← GroupName await model.generateText({ model: "deepseek-v4-flash", // ← concrete model id messages: [...] }); ``` ### Decision procedure (when the user names a specific model) 1. The user says "use DeepSeek v3.2" / "use hunyuan instruct" / "use Kimi k2.6" / "use GLM-5" / … 2. `createModel("cloudbase")` stays the same. 3. Put the model id into the **`model` field**: `{ model: "deepseek-v3.2" }`, `{ model: "hunyuan-2.0-instruct-20251111" }`, `{ model: "kimi-k2.6" }`, `{ model: "glm-5" }`, … 4. **Never assume the model is already enabled.** Before writing the SDK call, verify it is present in `DescribeAIModels({ GroupName: "cloudbase" }).Models[]`. If missing, call `DescribeManagedAIModelList` to confirm the exact `Model` name the platform supports (case-sensitive — do **not** guess the spelling), then enable it via `UpdateAIModel` with `Status: 1` (remember `Models` is a full replacement, so resend everything already enabled + the new one). > If you are about to type `ai.createModel(` and the thing inside the parentheses is a vendor name, a model name, or a guess — **stop**. It is almost certainly one of the three legal values above. --- ## Mandatory Two-Step Preflight (before any SDK code) Before generating any AI-related SDK code, **run the two-step preflight**: ① eligibility, ② group readiness. Emitting `createModel(...)` straight away and letting the user debug runtime errors is significantly more costly. ### Step 0: obtain the environment ID Call the MCP tool `queryEnv` with `action=info` and read `EnvId` from the response. Every subsequent check and purchase link uses this `EnvId`. --- ### Preflight ① — Eligibility (Token Credits resource pack) Call the MCP tool: ``` callCloudApi(service="tcb", action="DescribeEnvPostpayPackage", params={ EnvId }) ``` **Pass conditions (all required):** - `envPostpayPackageInfoList` contains at least one entry - That entry's `postpayPackageId` starts with `pkg_tcb_tokencredits_` - That entry's `status` is NOT in `[3, 4]` (3 / 4 typically mean expired / disabled; trust the live response) - ❌ **Not satisfied** → **stop writing code** and surface this to the user (replacing `{envId}` with the real id): > The current environment has no active Token Credits resource pack. Please purchase one before calling any AI API: > https://buy.cloud.tencent.com/lowcode?buyType=resPack&envId={envId}&resourceType=token > > Let me know once it's done and I'll re-check the resource pack status. - ✅ **Satisfied** → proceed to preflight ②. > Parameter casing is PascalCase by contract. If the call returns `InvalidParameter`, fall back to camelCase (`envId` / `envPostpayPackageInfoList`) and trust the live response. For the Mini Program scenario there is an additional growth-plan branch — switch to the `ai-model-wechat` skill. --- ### Preflight ② — Group readiness (`DescribeAIModels` → `UpdateAIModel` if needed) Eligibility alone is not enough. **Do not write `createModel("cloudbase")` yet.** First confirm that the target `GroupName` exists in the env with `Status=1`, and that the target `Model` is present in its `Models[]`. 1. **List groups configured in the current env:** ``` callCloudApi(service="tcb", action="DescribeAIModels", params={ EnvId }) ``` Returns `AIModelGroups: AIModelGroup[]`, where each `AIModelGroup` includes `GroupName`, `Type` (`builtin` / `custom`), `Models: [{ Model, EnableMCP, Tags }]`, `Status` (1 = on / 2 = off), `BaseUrl`, `Secret`, `Remark`. The main managed `GroupName` is `cloudbase`. 2. **Never assume a model is already enabled.** Inspect `AIModelGroups[?].Models[].Model` for the `cloudbase` group. If the target model (or, when the user did not specify one, the model you intend to default to such as `deepseek-v4-flash`) is missing, jump to step 4 and enable it — do not call `createModel("cloudbase")` yet. If the `cloudbase` group itself is missing or has `Status=2`, also jump to step 4. 3. **User asked for a model that belongs to the managed catalog** (e.g. `deepseek-v3.2`, `hunyuan-2.0-instruct-20251111`, `glm-5`, `kimi-k2.6`, …): check whether that `Model` is already in the `cloudbase` group's `Models[]`. If not, jump to step 4. **Do not guess the exact model id** — verify the canonical spelling in `DescribeManagedAIModelList` first (step 4 covers this). 4. **Enable / add a managed model** (always inspect the authoritative catalog + pricing first): ``` callCloudApi(service="tcb", action="DescribeManagedAIModelList", params={ EnvId }) ``` Returns `ManagedAIModelGroup[]`, where each group lists `GroupName` (e.g. `cloudbase`), `Remark`, and `Models: [{ Model, EnableMCP, ModelSpec{ContextLength, MaxInputToken, MaxOutputToken}, ModelChargingInfo[{Type, InputPrice, OutputPrice, InputOutputUnit, CachePrice}] }]`. **This is the single source of truth for supported model names and pricing — do not infer them from memory. Use the exact `Model` string returned here when calling `UpdateAIModel`.** Also surface the prices to the user before enabling. Then enable (note: `Models` is a **full replacement** — always resend the already-enabled models together with the new one): ``` callCloudApi(service="tcb", action="UpdateAIModel", params={ EnvId, GroupName: "cloudbase", Models: [ // resend every model that DescribeAIModels already showed as enabled { Model: "<already-enabled model, e.g. deepseek-v4-flash>" }, // append the newly-requested one, using the exact spelling from DescribeManagedAIModelList { Model: "<target model>" } ], Status: 1 }) ``` 5. **The requested model is not in the managed catalog** (not found by `DescribeManagedAIModelList`) → jump to the next section, **Custom onboarding (models outside the managed catalog)**. > All Actions use `service=tcb`, `Version=2018-06-08`. Parameters are PascalCase (`EnvId` / `GroupName` / `Models` / `Status`). Fall back to camelCase only if the call returns `InvalidParameter`. --- ## Available Providers and Models `ai.createModel(<GroupName>)` accepts exactly three kinds of legal values: ### 1. `"cloudbase"` — the main managed group (recommended) - `GroupName: "cloudbase"`, `Type: "builtin"`, `Remark: "腾讯云开发"` (Tencent CloudBase) - Backed by **Tencent Cloud TokenHub**, a unified managed pool covering multiple vendors — **Hunyuan** (HY 2.0 Instruct, HY 2.0 Think, Hunyuan-role, Hy3 preview, …), **DeepSeek** (DeepSeek-V4-Pro, DeepSeek-V4-Flash, Deepseek-v3.2, Deepseek-v3.1, Deepseek-r1-0528, Deepseek-v3-0324, …), **Zhipu GLM** (GLM-5, GLM-5-Turbo, GLM-5.1, GLM-5V-Turbo), **Kimi** (K2.5, K2.6), **MiniMax** (M2.5, M2.7), and more. The roster evolves — **do not hard-code specific SKUs** in application code; discover at runtime. - **No model is enabled by default.** Always call `DescribeAIModels` first to see what the env has actually enabled; if your target model is missing, call `DescribeManagedAIModelList` for the authoritative catalog + pricing and then `UpdateAIModel` (`Status: 1`, `Models` full-replacement) to enable it before making the SDK call. - Authoritative catalog + pricing: `DescribeManagedAIModelList` - Env-enabled set: `DescribeAIModels` ### 2. `"hunyuan-exp"` — legacy builtin group (kept for compatibility) - Primarily relevant to the Mini Program Growth Plan scenario; do not use from Web unless the env explicitly still has it (switch to the `ai-model-wechat` skill for that flow) - Default model: `hunyuan-2.0-instruct-20251111`; additional hunyuan SKUs must be discovered at runtime via `DescribeAIModels({ GroupName: "hunyuan-exp" }).Models[]` — do not hard-code other IDs ### 3. User-defined GroupName - Onboarded via `CreateAIModel` (see the next section). The custom `GroupName` **MUST start with `custom-`** (e.g. `custom-kimi`, `custom-moonshot`, `custom-openai-compat`). This naming convention prevents future collisions with built-in / vendor GroupNames (`cloudbase`, `hunyuan-exp`, `deepseek`, `glm`, `kimi`, `minimax`, …) that the platform may introduce over time - Examples: `createModel("custom-kimi")`, `createModel("custom-openai-compat")` > **Never** write guesses like `createModel("deepseek")` or `createModel("custom")` unless `DescribeAIModels` explicitly returned that exact `GroupName` (old envs may still carry historical `deepseek` / `hunyuan-exp` builtin groups — that stays legal for compatibility, but new projects should always go through `cloudbase`). --- ## Custom onboarding (models outside the managed catalog) When the user wants to call a **non-managed** model (self-hosted, enterprise-internal, third-party OpenAI-compatible endpoint, …), **do not block**. Guide them through onboarding: ### Option 1: console flow (recommended, user handles it) `https://tcb.cloud.tencent.com/dev?envId={envId}#/ai` ### Option 2: programmatic onboarding (`CreateAIModel`) ``` callCloudApi(service="tcb", action="CreateAIModel", params={ EnvId: "<envId>", GroupName: "custom-<your-name>", // MUST start with "custom-" (e.g. custom-kimi, custom-openai-compat); never start with "cloudbase" BaseUrl: "<OpenAI-compatible endpoint, e.g. https://api.moonshot.cn/v1>", Models: [ { Model: "<model name, e.g. kimi-k2.5>", EnableMCP: true } ], Remark: "<optional remark>", Status: 1, Secret: { ApiKey: "<vendor api key supplied by the user>" } }) ``` Once onboarded, confirm with `DescribeAIModels` that the group is ready, then call `ai.createModel("<the GroupName you just registered>")` from your code. Use `UpdateAIModel` to add/remove models, rotate keys, or change `BaseUrl` (remember `Models` is a **full replacement**). Use `DeleteAIModel` to remove a custom group (builtin groups cannot be deleted). > Custom-model billing is covered by the third-party provider and does not draw from the Token Credits resource pack. Field casing follows the live contract — fall back to camelCase on `InvalidParameter`. --- ## Installation ```bash npm install @cloudbase/js-sdk ``` ## Initialization > ⚠️ **Do not use anonymous sign-in as the default.** Anonymous login is **disabled by default** for new environments, and inactive existing environments have also been automatically disabled. Even when anonymous login is manually enabled, **anonymous users are denied AI model invocation permissions by default**. The AI-model skill does **not** prescribe a specific login UI — delegate that concern: >