feat: add managed Granite embedding models
GoodBuddy previously required an external OpenAI-compatible endpoint for vector retrieval. It can now install, verify, import, remove, and run the built-in Granite multilingual embedding model in an isolated local inference process, while retaining custom embedding connections and migrating existing settings. The model connection UI now manages built-in and custom vector connections consistently, reports the actual retrieval path, preserves credentials across failed saves, and renders the vector tab synchronously. Related settings copy, switch styling, metadata typography, and renderer bundle guardrails are aligned with the resulting workflow. Release note: 新增可管理的内置 Granite 多语言向量模型，支持本地下载、离线导入、连接切换与诊断；同时优化向量设置及相关界面的文案、开关和状态显示。
This commit is contained in:
mesalogo
+154
-1
@@ -96,6 +96,7 @@ import type {
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} from './speech-model-contracts'
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import type {
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EmbeddingDiagnosticResult,
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EmbeddingModelSnapshot,
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EmbeddingSettingsSnapshot,
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KnowledgeEmbeddingIndexSnapshot
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} from './embedding-contracts'
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@@ -387,6 +388,11 @@ export type ImageGenerationQuality = z.infer<
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export const defaultModelProfileId =
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'00000000-0000-4000-8000-000000000001'
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export const modelProfileIdSchema = z.string().uuid()
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export const builtinEmbeddingConnectionId =
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'00000000-0000-4000-8000-000000000002'
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export const legacyEmbeddingConnectionId =
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'00000000-0000-4000-8000-000000000003'
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export const embeddingConnectionIdSchema = z.string().uuid()
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export const contextCompressionModelSourceSchema =
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z.discriminatedUnion('kind', [
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@@ -554,6 +560,35 @@ const modelProfileInputSchema = z
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})
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.strict()
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const embeddingConnectionInputSchema = z.discriminatedUnion('kind', [
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z
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.object({
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id: z.literal(builtinEmbeddingConnectionId),
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name: z.string().trim().min(1).max(64),
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kind: z.literal('builtin')
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})
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.strict(),
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z
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.object({
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id: embeddingConnectionIdSchema.refine(
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(id) => id !== builtinEmbeddingConnectionId,
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'系统向量连接 ID 不能用于用户连接'
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),
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name: z.string().trim().min(1).max(64),
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kind: z.literal('openai-compatible'),
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baseUrl: z.string().url().max(2_048),
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modelName: z
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.string()
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.trim()
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.min(1)
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.max(256)
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.regex(/^[\w./:-]+$/, '向量模型名称包含不支持的字符'),
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authentication: modelAuthenticationSchema,
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apiKey: modelApiKeyUpdateSchema
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})
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.strict()
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])
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export const runtimeModelSourceSchema = z.discriminatedUnion('kind', [
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z.object({ kind: z.literal('platform') }).strict(),
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z
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@@ -597,6 +632,13 @@ export const runtimeSettingsInputSchema = z
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.max(256)
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.regex(/^[\w./:-]+$/, '向量模型名称包含不支持的字符'),
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knowledgeEmbeddingApiKey: modelApiKeyUpdateSchema.optional(),
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embeddingConnections: z
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.array(embeddingConnectionInputSchema)
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.min(1)
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.max(20)
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.optional(),
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activeEmbeddingConnectionId:
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embeddingConnectionIdSchema.optional(),
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knowledgeRerankEnabled: z.boolean(),
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knowledgeRerankEndpoint: z.string().url().max(2_048),
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knowledgeRerankModel: z
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@@ -797,6 +839,76 @@ export const runtimeSettingsInputSchema = z
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message: '向量接口 URL 必须使用 HTTP 或 HTTPS'
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})
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}
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if (settings.embeddingConnections) {
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const ids = new Set(
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settings.embeddingConnections.map((connection) => connection.id)
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)
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const names = new Set(
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settings.embeddingConnections.map((connection) =>
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connection.name.toLowerCase()
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)
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)
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if (
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ids.size !== settings.embeddingConnections.length ||
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names.size !== settings.embeddingConnections.length
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) {
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context.addIssue({
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code: 'custom',
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path: ['embeddingConnections'],
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message: '向量连接的 ID 和名称必须唯一'
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})
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}
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if (
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!settings.embeddingConnections.some(
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(connection) =>
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connection.id === builtinEmbeddingConnectionId &&
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connection.kind === 'builtin'
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)
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) {
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context.addIssue({
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code: 'custom',
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path: ['embeddingConnections'],
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message: '系统向量连接不能删除'
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})
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}
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for (const [index, connection] of
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settings.embeddingConnections.entries()) {
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if (connection.kind === 'builtin') {
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continue
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}
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if (
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!['http:', 'https:'].includes(
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new URL(connection.baseUrl).protocol
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)
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) {
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context.addIssue({
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code: 'custom',
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path: ['embeddingConnections', index, 'baseUrl'],
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message: '向量接口 URL 必须使用 HTTP 或 HTTPS'
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})
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}
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if (
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connection.authentication === 'none' &&
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connection.apiKey.action === 'replace'
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) {
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context.addIssue({
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code: 'custom',
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path: ['embeddingConnections', index, 'apiKey'],
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message: '无认证向量连接不得配置 API Key'
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})
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}
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}
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const activeId =
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settings.activeEmbeddingConnectionId ??
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builtinEmbeddingConnectionId
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if (!ids.has(activeId)) {
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context.addIssue({
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code: 'custom',
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path: ['activeEmbeddingConnectionId'],
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message: '当前向量连接不存在'
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})
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}
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}
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if (
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!['http:', 'https:'].includes(
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new URL(settings.knowledgeRerankEndpoint).protocol
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@@ -828,6 +940,33 @@ export type ModelConnectionSettings = {
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credentialSource: 'none' | 'encrypted' | 'environment' | 'unreadable'
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}
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export type EmbeddingConnectionSettings =
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| {
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id: typeof builtinEmbeddingConnectionId
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name: string
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kind: 'builtin'
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apiKeyConfigured?: false
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credentialSource?: 'none'
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}
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| {
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id: string
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name: string
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kind: 'openai-compatible'
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baseUrl: string
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modelName: string
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authentication: ModelAuthentication
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apiKeyConfigured?: boolean
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credentialSource?:
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| 'none'
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| 'encrypted'
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| 'environment'
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| 'unreadable'
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apiKey?:
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| { action: 'keep' }
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| { action: 'replace'; value: string }
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| { action: 'clear' }
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}
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export type ConfiguredRuntimeSettings = {
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modelProfiles: ModelConnectionSettings[]
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opencodeBaseUrl: string
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@@ -866,6 +1005,8 @@ export type RuntimeSettings = {
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| 'encrypted'
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| 'environment'
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| 'unreadable'
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embeddingConnections?: EmbeddingConnectionSettings[]
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activeEmbeddingConnectionId?: string
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knowledgeRerankEnabled?: boolean
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knowledgeRerankEndpoint?: string
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knowledgeRerankModel?: string
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@@ -1448,7 +1589,19 @@ export type DesktopApi = {
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}
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embeddings?: {
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getSnapshot: () => Promise<EmbeddingSettingsSnapshot>
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diagnose: () => Promise<EmbeddingDiagnosticResult>
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diagnose: (connectionId: string) => Promise<EmbeddingDiagnosticResult>
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setCurrent: (
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connectionId: string
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) => Promise<EmbeddingSettingsSnapshot>
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installModel: (
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modelId: string,
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expectedDownloadSource: ModelDownloadSource
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) => Promise<EmbeddingModelSnapshot>
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cancelModelOperation: (modelId: string) => Promise<boolean>
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importModelArchive: (
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modelId: string
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) => Promise<EmbeddingModelSnapshot | undefined>
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removeModel: (modelId: string) => Promise<EmbeddingModelSnapshot>
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}
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documentParsing?: {
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getSnapshot: () => Promise<DocumentParsingSnapshot>
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@@ -3,6 +3,7 @@ import {
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embeddingDiagnosticResultSchema,
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embeddingIndexJobSchema,
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embeddingIndexStatusSchema,
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embeddingSettingsSnapshotSchema,
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knowledgeEmbeddingIndexSnapshotSchema,
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embeddingSafeErrorSchema,
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isEmbeddingIndexJobActive
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@@ -66,6 +67,48 @@ describe('embedding contracts', () => {
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).toBe(false)
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})
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it('keeps credentials, vectors, and Main-only paths out of settings snapshots', () => {
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const snapshot = {
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configuration: {
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provider: 'builtin',
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model: 'granite-embedding',
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credentialConfigured: false
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},
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connections: [
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{
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id: '00000000-0000-4000-8000-000000000002',
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name: 'GoodBuddy built-in',
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kind: 'builtin' as const,
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model: 'granite-embedding',
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credentialConfigured: false
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}
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],
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currentConnectionId:
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'00000000-0000-4000-8000-000000000002',
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models: {
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selectedDownloadSource: 'modelscope' as const,
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catalog: [],
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installed: [],
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operations: []
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}
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}
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expect(
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embeddingSettingsSnapshotSchema.safeParse(snapshot).success
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).toBe(true)
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for (const forbidden of [
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{ apiKey: 'secret' },
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{ vectors: [[0.1, 0.2]] },
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{ modelDirectory: 'C:\\private\\embedding' }
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]) {
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expect(
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embeddingSettingsSnapshotSchema.safeParse({
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...snapshot,
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...forbidden
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}).success
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).toBe(false)
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}
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})
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it('validates queued, running and terminal index jobs', () => {
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const base = {
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id: 'job-1',
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@@ -1,4 +1,9 @@
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import { z } from 'zod'
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import {
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MODEL_DOWNLOAD_SOURCES,
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modelDownloadAvailabilitySchema,
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modelDownloadSourceSchema
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} from './model-download-contracts'
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const boundedLabelSchema = z.string().trim().min(1).max(256)
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const timestampSchema = z.number().int().nonnegative().max(Number.MAX_SAFE_INTEGER)
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@@ -48,6 +53,120 @@ export type EmbeddingConfigurationSummary = z.infer<
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typeof embeddingConfigurationSummarySchema
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>
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export const embeddingConnectionIdRequestSchema = z
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.object({
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connectionId: z.string().uuid()
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})
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.strict()
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export const embeddingConnectionSummarySchema = z
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.object({
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id: z.string().uuid(),
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name: z.string().trim().min(1).max(64),
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kind: z.enum(['builtin', 'openai-compatible']),
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model: boundedLabelSchema,
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endpoint: safeProviderEndpointSchema.optional(),
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authentication: z.enum(['api-key', 'none']).optional(),
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credentialConfigured: z.boolean()
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})
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.strict()
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export type EmbeddingConnectionSummary = z.infer<
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typeof embeddingConnectionSummarySchema
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>
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export const embeddingModelIdSchema = z
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.string()
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.min(1)
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.max(96)
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.regex(/^[a-z0-9]+(?:-[a-z0-9]+)*$/u)
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const embeddingModelFileSchema = z
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.object({
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name: z.string().min(1).max(255),
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role: z.enum([
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'model',
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'tokenizer',
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'tokenizer-configuration',
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'configuration',
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'license'
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]),
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size: z.number().int().positive().safe(),
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sha256: z.string().regex(/^[a-f0-9]{64}$/u)
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})
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.strict()
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export const embeddingModelOperationSchema = z
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.object({
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modelId: embeddingModelIdSchema,
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kind: z.enum(['download', 'import']),
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phase: z.enum(['preparing', 'transferring', 'installing']),
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currentFile: z.string().min(1).max(255).nullable(),
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completedBytes: z.number().int().nonnegative().safe(),
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totalBytes: z.number().int().positive().safe().nullable(),
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downloadSource: modelDownloadSourceSchema.optional()
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})
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.strict()
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export const embeddingModelSnapshotSchema = z
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.object({
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selectedDownloadSource: modelDownloadSourceSchema,
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catalog: z.array(
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z
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.object({
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id: embeddingModelIdSchema,
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displayName: z.string().trim().min(1).max(120),
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description: z.string().trim().min(1).max(1_000),
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languages: z.array(z.string().trim().min(1).max(80)).min(1).max(32),
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runtime: z.literal('onnxruntime-web/wasm'),
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dimensions: z.number().int().positive().max(65_536),
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contextTokens: z.number().int().positive().max(10_000_000),
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quantization: z.string().trim().min(1).max(40),
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recommended: z.boolean(),
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available: z.boolean(),
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unavailableReason: z.string().trim().min(1).max(1_000).optional(),
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license: z
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.object({
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name: z.string().trim().min(1).max(120),
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notice: z.string().trim().min(1).max(1_000),
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url: z.url()
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})
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.strict(),
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files: z.array(embeddingModelFileSchema).min(1).max(32),
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downloadAvailability: z
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.array(modelDownloadAvailabilitySchema)
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.length(MODEL_DOWNLOAD_SOURCES.length)
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})
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.strict()
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),
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installed: z.array(
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z
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.object({
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id: embeddingModelIdSchema,
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displayName: z.string().trim().min(1).max(120),
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source: z.enum(['download', 'local']),
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installedAt: z.string().datetime(),
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files: z.array(embeddingModelFileSchema).min(1).max(32)
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})
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.strict()
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),
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operations: z.array(embeddingModelOperationSchema)
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})
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.strict()
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export type EmbeddingModelSnapshot = z.infer<
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typeof embeddingModelSnapshotSchema
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>
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export const embeddingModelActionInputSchema = z
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.object({ modelId: embeddingModelIdSchema })
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.strict()
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export const embeddingModelInstallInputSchema = z
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.object({
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modelId: embeddingModelIdSchema,
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expectedDownloadSource: modelDownloadSourceSchema
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})
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.strict()
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const embeddingDiagnosticBase = {
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provider: boundedLabelSchema,
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model: boundedLabelSchema,
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@@ -180,7 +299,10 @@ export type EmbeddingIndexStatus = z.infer<
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export const embeddingSettingsSnapshotSchema = z
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.object({
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configuration: embeddingConfigurationSummarySchema
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configuration: embeddingConfigurationSummarySchema,
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connections: z.array(embeddingConnectionSummarySchema),
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currentConnectionId: z.string().uuid(),
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models: embeddingModelSnapshotSchema
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})
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.strict()
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export type EmbeddingSettingsSnapshot = z.infer<
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@@ -67,6 +67,12 @@ export const ipcChannels = {
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speechTranscriptionCancel: 'speech:transcription:cancel',
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embeddingSettingsGet: 'settings:embedding:get',
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embeddingDiagnose: 'settings:embedding:diagnose',
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embeddingSetCurrent: 'settings:embedding:set-current',
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embeddingModelsInstall: 'settings:embedding-models:install',
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embeddingModelsCancel: 'settings:embedding-models:cancel',
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embeddingModelsImportArchive:
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'settings:embedding-models:import-archive',
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embeddingModelsRemove: 'settings:embedding-models:remove',
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documentParsingGet: 'settings:document-parsing:get',
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documentOcrModelsProgress:
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'settings:document-ocr-models:progress',
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Reference in New Issue
Block a user