feat: add server model catalog
This commit is contained in:
Jesse_Chen
+13
-7
@@ -72,14 +72,20 @@ NEXT_PUBLIC_SUPABASE_ANON_KEY=...
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SUPABASE_SERVICE_ROLE_KEY=...
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ADMIN_EMAILS=...
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# Either OpenAI:
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OPENAI_API_KEY=...
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MASTRA_MODEL=...
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# Recommended multi-model catalog. The JSON references server-only keys.
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LLM_DEFAULT_MODEL_ID=deepseek-pro
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LLM_MODELS_JSON='[{"id":"deepseek-pro","label":"DeepSeek V4 Pro","description":"更适合复杂分析","provider":"openai-compatible","baseURL":"https://api.deepseek.com","apiKeyEnv":"DEEPSEEK_API_KEY","model":"deepseek-v4-pro","creditCost":1},{"id":"gpt-5-mini","label":"ChatGPT 5 Mini","description":"响应稳定、速度均衡","provider":"openai","apiKeyEnv":"OPENAI_API_KEY","model":"openai/gpt-5-mini","creditCost":1}]'
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DEEPSEEK_API_KEY=<server-secret>
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OPENAI_API_KEY=<server-secret>
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# Or an OpenAI-compatible provider:
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LLM_BASE_URL=...
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LLM_API_KEY=...
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LLM_MODEL=...
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# Legacy single-model OpenAI configuration remains supported:
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# OPENAI_API_KEY=<server-secret>
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# MASTRA_MODEL=openai/gpt-5-mini
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# Legacy single OpenAI-compatible provider remains supported:
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# LLM_BASE_URL=https://provider.example/v1
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# LLM_API_KEY=<server-secret>
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# LLM_MODEL=provider-model-id
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# Optional VedAstro official upstream; local fallback remains available:
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VEDASTRO_API_ENDPOINT=...
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@@ -27,7 +27,6 @@
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**Files:**
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- Modify: `frontend/src/mastra/model.ts`
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- Create: `frontend/tests/model-catalog.test.ts`
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- Modify: `frontend/.env.example`
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- Modify: `frontend/README.md`
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- Modify: `deploy/README.md`
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@@ -132,12 +131,12 @@ Expected: all catalog tests PASS and TypeScript exits `0`.
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- [ ] **Step 5: Document configuration**
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Update `.env.example`, `frontend/README.md`, and `deploy/README.md` with `LLM_MODELS_JSON`, `LLM_DEFAULT_MODEL_ID`, one secret environment variable per provider, and the existing single-model fallback. Use redacted values only.
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Update `frontend/README.md` and `deploy/README.md` with `LLM_MODELS_JSON`, `LLM_DEFAULT_MODEL_ID`, one secret environment variable per provider, and the existing single-model fallback. Use redacted values only. Do not add `frontend/.env.example`: the repository intentionally ignores all `.env*` files.
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- [ ] **Step 6: Commit the catalog task**
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```bash
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git add frontend/src/mastra/model.ts frontend/tests/model-catalog.test.ts frontend/.env.example frontend/README.md deploy/README.md
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git add frontend/src/mastra/model.ts frontend/tests/model-catalog.test.ts frontend/README.md deploy/README.md docs/superpowers/plans/2026-07-17-multi-model-chat-selection.md
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git commit -m "feat: add server model catalog"
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```
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+15
-12
@@ -39,24 +39,27 @@ cp .env.example .env.local
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# Python 占星计算服务
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JYOTISH_API_BASE=http://127.0.0.1:5200
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# 方案 A：默认 OpenAI
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OPENAI_API_KEY=sk-...
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MASTRA_MODEL=openai/gpt-5-mini
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# 推荐：多模型目录。目录只保存路由元数据，Key 由 apiKeyEnv 引用。
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LLM_DEFAULT_MODEL_ID=deepseek-pro
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LLM_MODELS_JSON='[{"id":"deepseek-pro","label":"DeepSeek V4 Pro","description":"更适合复杂分析","provider":"openai-compatible","baseURL":"https://api.deepseek.com","apiKeyEnv":"DEEPSEEK_API_KEY","model":"deepseek-v4-pro","creditCost":1},{"id":"gpt-5-mini","label":"ChatGPT 5 Mini","description":"响应稳定、速度均衡","provider":"openai","apiKeyEnv":"OPENAI_API_KEY","model":"openai/gpt-5-mini","creditCost":1}]'
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DEEPSEEK_API_KEY=<server-secret>
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OPENAI_API_KEY=<server-secret>
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# 方案 B：任意 OpenAI-compatible 第三方模型
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# 只要填写任一 LLM_* 项，应用便会优先使用本方案。
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# Base URL 通常填写到 /v1，不要填写完整 /chat/completions 地址。
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LLM_BASE_URL=https://your-provider.example/v1
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LLM_API_KEY=your-secret-key
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LLM_MODEL=your-model-id
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# 可选：只作为 Mastra 内部标签，不影响请求地址
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LLM_PROVIDER_ID=third-party
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# 兼容旧的单模型 OpenAI 配置
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# OPENAI_API_KEY=<server-secret>
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# MASTRA_MODEL=openai/gpt-5-mini
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# 兼容旧的单个 OpenAI-compatible 配置
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# LLM_BASE_URL=https://your-provider.example/v1
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# LLM_API_KEY=<server-secret>
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# LLM_MODEL=your-model-id
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# LLM_PROVIDER_ID=third-party
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# 可选：部署目录与本仓结构不同时，显式指定 Mastra Skill 目录
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# JYOTISH_SKILL_PATH=/absolute/path/to/yinduzhanxing/skills/jyotish-vedic-astrology
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```
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第三方端点必须兼容 OpenAI 的 Chat Completions 调用方式，并支持工具调用（function calling），否则 Agent 无法稳定调用占星计算工具。密钥只放在 `.env.local`，**不要**加 `NEXT_PUBLIC_` 前缀，也不要提交到 Git。每次修改 `.env.local` 后重启 Next.js 开发服务器。
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第三方端点必须兼容 OpenAI 的 Chat Completions 调用方式，并支持工具调用（function calling），否则 Agent 无法稳定调用占星计算工具。`LLM_MODELS_JSON` 只能填写服务端认可的固定地址和模型；浏览器只会得到模型 ID、名称、说明和点数。密钥只放在 `.env.local`，**不要**加 `NEXT_PUBLIC_` 前缀，也不要提交到 Git。每次修改 `.env.local` 后重启 Next.js 开发服务器。
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## Skill 如何触发
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+227
-52
@@ -1,69 +1,244 @@
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import type { MastraModelConfig } from "@mastra/core/llm";
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import { z } from "zod";
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type Environment = Readonly<Record<string, string | undefined>>;
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type LanguageModelMode = "openai" | "compatible";
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type LanguageModelSettings = {
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mode: LanguageModelMode;
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model: MastraModelConfig;
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configured: boolean;
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missing: string[];
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export type PublicLanguageModel = {
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readonly id: string;
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readonly label: string;
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readonly description: string;
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readonly creditCost: 1;
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readonly isDefault: boolean;
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};
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function environmentValue(name: string) {
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return process.env[name]?.trim() ?? "";
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export type ResolvedLanguageModel = PublicLanguageModel & {
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readonly mode: LanguageModelMode;
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readonly model: MastraModelConfig;
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};
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export type LanguageModelCatalog = {
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readonly models: readonly ResolvedLanguageModel[];
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readonly publicModels: readonly PublicLanguageModel[];
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readonly defaultModelId: string | null;
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readonly issues: readonly string[];
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};
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const modelIdSchema = z.string().trim().min(1).max(64).regex(/^[a-z0-9][a-z0-9._-]*$/);
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const apiKeyEnvironmentNameSchema = z.string().regex(/^[A-Z][A-Z0-9_]*$/);
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const sharedCatalogFields = {
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id: modelIdSchema,
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label: z.string().trim().min(1).max(60),
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description: z.string().trim().max(100).default(""),
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apiKeyEnv: apiKeyEnvironmentNameSchema,
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model: z.string().trim().min(1).max(120),
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creditCost: z.literal(1),
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};
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const catalogEntrySchema = z.discriminatedUnion("provider", [
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z.object({
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...sharedCatalogFields,
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provider: z.literal("openai"),
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}).strict(),
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z.object({
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...sharedCatalogFields,
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provider: z.literal("openai-compatible"),
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baseURL: z.string().url().refine((value) => value.startsWith("https://")),
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}).strict(),
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]);
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type CatalogEntry = z.infer<typeof catalogEntrySchema>;
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function environmentValue(environment: Environment, name: string) {
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return environment[name]?.trim() ?? "";
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}
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/**
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* Resolves either the default OpenAI model or a third-party endpoint that
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* implements the OpenAI Chat Completions API. A supplied LLM_* value switches
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* the app to compatible-provider mode, so the old OPENAI_* setup remains
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* backwards compatible.
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*/
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function resolveLanguageModelSettings(): LanguageModelSettings {
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const baseURL = environmentValue("LLM_BASE_URL");
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const apiKey = environmentValue("LLM_API_KEY");
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const modelId = environmentValue("LLM_MODEL");
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const hasCompatibleSetting = Boolean(baseURL || apiKey || modelId);
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if (hasCompatibleSetting) {
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const missing = [
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!baseURL && "LLM_BASE_URL",
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!apiKey && "LLM_API_KEY",
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!modelId && "LLM_MODEL",
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].filter((value): value is string => Boolean(value));
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return {
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mode: "compatible",
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configured: missing.length === 0,
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missing,
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model: {
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// This is an internal label for Mastra. It does not need to match the
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// provider's company name; the URL determines the actual endpoint.
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providerId: environmentValue("LLM_PROVIDER_ID") || "third-party",
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modelId: modelId || "not-configured",
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url: baseURL || undefined,
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apiKey: apiKey || undefined,
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},
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};
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}
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const openAIKey = environmentValue("OPENAI_API_KEY");
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function publicModel(model: ResolvedLanguageModel): PublicLanguageModel {
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return {
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mode: "openai",
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configured: Boolean(openAIKey),
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missing: openAIKey ? [] : ["OPENAI_API_KEY"],
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model: environmentValue("MASTRA_MODEL") || "openai/gpt-5-mini",
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id: model.id,
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label: model.label,
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description: model.description,
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creditCost: model.creditCost,
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isDefault: model.isDefault,
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};
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}
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export const languageModelSettings = resolveLanguageModelSettings();
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function resolveCatalogEntry(
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entry: CatalogEntry,
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apiKey: string,
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isDefault: boolean,
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): ResolvedLanguageModel {
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const shared = {
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id: entry.id,
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label: entry.label,
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description: entry.description,
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creditCost: entry.creditCost,
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isDefault,
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} as const;
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export function languageModelConfigurationMessage() {
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if (languageModelSettings.configured) return null;
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switch (entry.provider) {
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case "openai":
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return {
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...shared,
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mode: "openai",
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model: entry.model,
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};
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case "openai-compatible":
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return {
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...shared,
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mode: "compatible",
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model: {
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providerId: entry.id,
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modelId: entry.model,
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url: entry.baseURL,
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apiKey,
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},
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};
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}
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}
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if (languageModelSettings.mode === "compatible") {
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return `第三方模型配置不完整：${languageModelSettings.missing.join("、")}`;
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function resolveExplicitCatalog(environment: Environment, rawCatalog: string): LanguageModelCatalog {
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let parsed: unknown;
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try {
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parsed = JSON.parse(rawCatalog);
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} catch {
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return { models: [], publicModels: [], defaultModelId: null, issues: ["catalog_json_invalid"] };
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}
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return "未配置 OPENAI_API_KEY";
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if (!Array.isArray(parsed)) {
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return { models: [], publicModels: [], defaultModelId: null, issues: ["catalog_not_array"] };
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}
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const defaultModelId = environmentValue(environment, "LLM_DEFAULT_MODEL_ID");
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const issues: string[] = [];
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const seenIds = new Set<string>();
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const models: ResolvedLanguageModel[] = [];
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parsed.forEach((value, index) => {
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const parsedEntry = catalogEntrySchema.safeParse(value);
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if (!parsedEntry.success) {
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issues.push(`catalog_entry_invalid:${index}`);
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return;
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}
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if (seenIds.has(parsedEntry.data.id)) {
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issues.push(`catalog_entry_duplicate:${index}`);
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return;
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}
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seenIds.add(parsedEntry.data.id);
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const apiKey = environmentValue(environment, parsedEntry.data.apiKeyEnv);
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if (!apiKey) {
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issues.push(`catalog_entry_secret_missing:${index}`);
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return;
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}
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models.push(resolveCatalogEntry(parsedEntry.data, apiKey, parsedEntry.data.id === defaultModelId));
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});
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const resolvedDefault = models.some((model) => model.id === defaultModelId)
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? defaultModelId
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: null;
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if (!resolvedDefault) issues.push("default_model_unavailable");
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return {
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models,
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publicModels: models.map(publicModel),
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defaultModelId: resolvedDefault,
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issues,
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};
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}
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function resolveLegacyCatalog(environment: Environment): LanguageModelCatalog {
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const baseURL = environmentValue(environment, "LLM_BASE_URL");
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const apiKey = environmentValue(environment, "LLM_API_KEY");
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const modelId = environmentValue(environment, "LLM_MODEL");
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const hasCompatibleSetting = Boolean(baseURL || apiKey || modelId);
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if (hasCompatibleSetting) {
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if (!baseURL || !apiKey || !modelId || !baseURL.startsWith("https://")) {
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return {
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models: [],
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publicModels: [],
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defaultModelId: null,
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issues: ["legacy_compatible_incomplete"],
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};
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}
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const model: ResolvedLanguageModel = {
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id: "legacy-compatible",
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label: modelId,
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description: "当前默认模型",
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creditCost: 1,
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isDefault: true,
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mode: "compatible",
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model: {
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providerId: environmentValue(environment, "LLM_PROVIDER_ID") || "third-party",
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modelId,
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url: baseURL,
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apiKey,
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},
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};
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return {
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models: [model],
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publicModels: [publicModel(model)],
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defaultModelId: model.id,
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issues: [],
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};
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}
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const openAIKey = environmentValue(environment, "OPENAI_API_KEY");
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if (!openAIKey) {
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return { models: [], publicModels: [], defaultModelId: null, issues: ["model_not_configured"] };
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}
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const modelIdValue = environmentValue(environment, "MASTRA_MODEL") || "openai/gpt-5-mini";
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const model: ResolvedLanguageModel = {
|
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id: "legacy-openai",
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label: modelIdValue.replace(/^openai\//, ""),
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description: "当前默认模型",
|
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creditCost: 1,
|
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isDefault: true,
|
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mode: "openai",
|
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model: modelIdValue,
|
||||
};
|
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return {
|
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models: [model],
|
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publicModels: [publicModel(model)],
|
||||
defaultModelId: model.id,
|
||||
issues: [],
|
||||
};
|
||||
}
|
||||
|
||||
export function resolveLanguageModelCatalog(environment: Environment): LanguageModelCatalog {
|
||||
const rawCatalog = environmentValue(environment, "LLM_MODELS_JSON");
|
||||
return rawCatalog
|
||||
? resolveExplicitCatalog(environment, rawCatalog)
|
||||
: resolveLegacyCatalog(environment);
|
||||
}
|
||||
|
||||
export const languageModelCatalog = resolveLanguageModelCatalog(process.env);
|
||||
|
||||
export function resolveLanguageModel(modelId: string) {
|
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return languageModelCatalog.models.find((model) => model.id === modelId) ?? null;
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||||
}
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|
||||
export function defaultLanguageModel() {
|
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const defaultModelId = languageModelCatalog.defaultModelId;
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return defaultModelId ? resolveLanguageModel(defaultModelId) : null;
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||||
}
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||||
|
||||
export function publicLanguageModelCatalog() {
|
||||
return {
|
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models: languageModelCatalog.publicModels,
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||||
defaultModelId: languageModelCatalog.defaultModelId,
|
||||
};
|
||||
}
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||||
|
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const configuredDefaultModel = defaultLanguageModel();
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||||
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export const languageModelSettings = {
|
||||
mode: configuredDefaultModel?.mode ?? "openai",
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||||
model: configuredDefaultModel?.model ?? "openai/gpt-5-mini",
|
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configured: Boolean(configuredDefaultModel),
|
||||
missing: languageModelCatalog.issues,
|
||||
} as const;
|
||||
|
||||
export function languageModelConfigurationMessage() {
|
||||
return configuredDefaultModel ? null : "未配置可用的语言模型";
|
||||
}
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||||
|
||||
@@ -0,0 +1,127 @@
|
||||
import assert from "node:assert/strict";
|
||||
import test from "node:test";
|
||||
import { resolveLanguageModelCatalog } from "../src/mastra/model.ts";
|
||||
|
||||
const configuredModels = [
|
||||
{
|
||||
id: "deepseek-pro",
|
||||
label: "DeepSeek V4 Pro",
|
||||
description: "复杂分析",
|
||||
provider: "openai-compatible",
|
||||
baseURL: "https://api.deepseek.com",
|
||||
apiKeyEnv: "DEEPSEEK_API_KEY",
|
||||
model: "deepseek-v4-pro",
|
||||
creditCost: 1,
|
||||
},
|
||||
{
|
||||
id: "gpt-mini",
|
||||
label: "ChatGPT Mini",
|
||||
description: "均衡响应",
|
||||
provider: "openai",
|
||||
apiKeyEnv: "OPENAI_API_KEY",
|
||||
model: "openai/gpt-5-mini",
|
||||
creditCost: 1,
|
||||
},
|
||||
] as const;
|
||||
|
||||
test("resolves configured models while returning sanitized public metadata", () => {
|
||||
// Given
|
||||
const environment = {
|
||||
LLM_DEFAULT_MODEL_ID: "deepseek-pro",
|
||||
LLM_MODELS_JSON: JSON.stringify(configuredModels),
|
||||
DEEPSEEK_API_KEY: "deepseek-secret",
|
||||
OPENAI_API_KEY: "openai-secret",
|
||||
};
|
||||
|
||||
// When
|
||||
const catalog = resolveLanguageModelCatalog(environment);
|
||||
|
||||
// Then
|
||||
assert.equal(catalog.defaultModelId, "deepseek-pro");
|
||||
assert.deepEqual(catalog.publicModels[0], {
|
||||
id: "deepseek-pro",
|
||||
label: "DeepSeek V4 Pro",
|
||||
description: "复杂分析",
|
||||
creditCost: 1,
|
||||
isDefault: true,
|
||||
});
|
||||
assert.equal(JSON.stringify(catalog.publicModels).includes("secret"), false);
|
||||
assert.equal(JSON.stringify(catalog.publicModels).includes("baseURL"), false);
|
||||
assert.equal(catalog.models[1]?.model, "openai/gpt-5-mini");
|
||||
});
|
||||
|
||||
test("excludes an invalid catalog entry without leaking its secret", () => {
|
||||
// Given
|
||||
const environment = {
|
||||
LLM_DEFAULT_MODEL_ID: "gpt-mini",
|
||||
LLM_MODELS_JSON: JSON.stringify([
|
||||
configuredModels[1],
|
||||
{
|
||||
...configuredModels[0],
|
||||
id: "broken model",
|
||||
baseURL: "http://api.deepseek.com",
|
||||
},
|
||||
]),
|
||||
OPENAI_API_KEY: "openai-secret",
|
||||
DEEPSEEK_API_KEY: "must-not-appear",
|
||||
};
|
||||
|
||||
// When
|
||||
const catalog = resolveLanguageModelCatalog(environment);
|
||||
|
||||
// Then
|
||||
assert.deepEqual(catalog.models.map((model) => model.id), ["gpt-mini"]);
|
||||
assert.equal(catalog.issues.length, 1);
|
||||
assert.equal(JSON.stringify(catalog.issues).includes("must-not-appear"), false);
|
||||
});
|
||||
|
||||
test("does not choose an undeclared default model", () => {
|
||||
// Given
|
||||
const environment = {
|
||||
LLM_DEFAULT_MODEL_ID: "removed-model",
|
||||
LLM_MODELS_JSON: JSON.stringify([configuredModels[1]]),
|
||||
OPENAI_API_KEY: "openai-secret",
|
||||
};
|
||||
|
||||
// When
|
||||
const catalog = resolveLanguageModelCatalog(environment);
|
||||
|
||||
// Then
|
||||
assert.equal(catalog.defaultModelId, null);
|
||||
assert.equal(catalog.issues.includes("default_model_unavailable"), true);
|
||||
});
|
||||
|
||||
test("derives the shipped compatible-provider configuration when no catalog exists", () => {
|
||||
// Given
|
||||
const environment = {
|
||||
LLM_BASE_URL: "https://api.deepseek.com",
|
||||
LLM_API_KEY: "legacy-secret",
|
||||
LLM_MODEL: "deepseek-v4-pro",
|
||||
LLM_PROVIDER_ID: "deepseek",
|
||||
};
|
||||
|
||||
// When
|
||||
const catalog = resolveLanguageModelCatalog(environment);
|
||||
|
||||
// Then
|
||||
assert.equal(catalog.defaultModelId, "legacy-compatible");
|
||||
assert.equal(catalog.models[0]?.id, "legacy-compatible");
|
||||
assert.equal(catalog.publicModels[0]?.label, "deepseek-v4-pro");
|
||||
assert.equal(JSON.stringify(catalog.publicModels).includes("legacy-secret"), false);
|
||||
});
|
||||
|
||||
test("reports an incomplete legacy provider without inventing a model", () => {
|
||||
// Given
|
||||
const environment = {
|
||||
LLM_BASE_URL: "https://api.deepseek.com",
|
||||
LLM_MODEL: "deepseek-v4-pro",
|
||||
};
|
||||
|
||||
// When
|
||||
const catalog = resolveLanguageModelCatalog(environment);
|
||||
|
||||
// Then
|
||||
assert.equal(catalog.models.length, 0);
|
||||
assert.equal(catalog.defaultModelId, null);
|
||||
assert.equal(catalog.issues.includes("legacy_compatible_incomplete"), true);
|
||||
});
|
||||
Reference in New Issue
Block a user