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
+233
-30
@@ -119,6 +119,10 @@ import {
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speechModelSelectionInputSchema
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} from '../shared/speech-model-contracts'
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import {
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embeddingConnectionIdRequestSchema,
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embeddingModelActionInputSchema,
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embeddingModelInstallInputSchema,
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embeddingModelSnapshotSchema,
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embeddingSettingsSnapshotSchema,
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knowledgeEmbeddingIndexCancelRequestSchema,
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knowledgeEmbeddingIndexRequestSchema,
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@@ -256,6 +260,8 @@ import type { SpeechModelManager } from './speech/speech-model-manager'
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import type { SpeechTranscriptionService } from './speech/speech-transcription-service'
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import { diagnoseEmbeddingProvider } from './knowledge/embedding-index-coordinator'
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import type { EmbeddingIndexCoordinator } from './knowledge/embedding-index-coordinator'
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import type { EmbeddingModelManager } from './knowledge/embedding-model-manager'
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import type { EmbeddingProvider } from './knowledge/types'
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import type { DocumentParsingService } from './document-parsing-service'
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import type { DocumentOcrModelManager } from './document-ocr-model-manager'
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import type { DocumentOcrBroker } from './document-ocr-broker'
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@@ -264,7 +270,6 @@ import type {
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GoodBuddyConfigApplyEvent,
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GoodBuddyConfigService
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} from './goodbuddy-config-service'
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import { OpenAIEmbeddingClient } from './knowledge/openai-embedding-client'
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import {
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magicNotePlainText,
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validateMagicNoteRichContent
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@@ -915,7 +920,19 @@ export function registerIpcHandlers(
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releaseNotesService?: ReleaseNotesService,
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goodbuddyConfigService?: GoodBuddyConfigService,
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runtimeExtensionStore?: RuntimeExtensionStore,
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shortcutSettingsService?: ShortcutSettingsService
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shortcutSettingsService?: ShortcutSettingsService,
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embeddingModelManager?: EmbeddingModelManager & {
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importArchive?(
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modelId: string,
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archivePath: string
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): Promise<unknown>
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},
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resolveEmbeddingProvider?: (
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connectionId: string
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) => Promise<EmbeddingProvider>,
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setCurrentEmbeddingConnection?: (
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connectionId: string
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) => Promise<void>
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): () => Promise<void> {
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type ActiveRequestLease = {
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controller: AbortController
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@@ -4339,38 +4356,209 @@ export function registerIpcHandlers(
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}
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)
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const requireEmbeddingProvider = async (): Promise<OpenAIEmbeddingClient> => {
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const requireEmbeddingProvider = async (
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connectionId: string
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): Promise<EmbeddingProvider> => {
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const settings = await settingsStore.getResolvedSettings()
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if (!settings.knowledgeEmbeddingEnabled) {
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throw new Error('请先启用并保存向量模型设置')
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}
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return new OpenAIEmbeddingClient({
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endpoint: settings.knowledgeEmbeddingBaseUrl,
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model: settings.knowledgeEmbeddingModel,
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apiKey: settings.knowledgeEmbeddingApiKey
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if (
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!settings.embeddingConnections?.some(
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(connection) => connection.id === connectionId
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)
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) {
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throw new Error('当前向量连接不存在')
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}
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if (!resolveEmbeddingProvider) {
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throw new Error('向量模型服务不可用')
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}
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return resolveEmbeddingProvider(connectionId)
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}
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const getEmbeddingSettingsSnapshot = async () => {
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if (!embeddingModelManager) {
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throw new Error('内置向量模型服务不可用')
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}
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const settings = await settingsStore.getPublicSettings()
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const models = await embeddingModelManager.getSnapshot()
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const connection = settings.embeddingConnections?.find(
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(candidate) =>
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candidate.id === settings.activeEmbeddingConnectionId
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)
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if (!connection || !settings.activeEmbeddingConnectionId) {
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throw new Error('当前向量连接不存在')
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}
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const builtinModel =
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models.catalog.find((model) => model.recommended) ??
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models.catalog[0]
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if (!builtinModel) {
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throw new Error('内置向量模型目录为空')
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}
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return embeddingSettingsSnapshotSchema.parse({
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configuration:
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connection?.kind === 'builtin'
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? {
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provider: 'builtin',
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model: builtinModel.id,
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credentialConfigured: false
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}
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: {
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provider: 'openai-compatible',
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model:
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connection?.modelName ??
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settings.knowledgeEmbeddingModel,
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endpoint:
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connection?.baseUrl ??
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settings.knowledgeEmbeddingBaseUrl,
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credentialConfigured:
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connection?.apiKeyConfigured ??
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settings.knowledgeEmbeddingApiKeyConfigured
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},
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connections: (settings.embeddingConnections ?? []).map(
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(candidate) =>
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candidate.kind === 'builtin'
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? {
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id: candidate.id,
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name: candidate.name,
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kind: candidate.kind,
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model: builtinModel.id,
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credentialConfigured: false
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}
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: {
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id: candidate.id,
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name: candidate.name,
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kind: candidate.kind,
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model: candidate.modelName,
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endpoint: candidate.baseUrl,
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authentication: candidate.authentication,
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credentialConfigured:
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candidate.apiKeyConfigured ?? false
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}
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),
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currentConnectionId: settings.activeEmbeddingConnectionId,
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models
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})
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}
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registerHandler(ipcChannels.embeddingSettingsGet, async (event) => {
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assertTrustedSender(event, window)
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const settings = await settingsStore.getPublicSettings()
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return embeddingSettingsSnapshotSchema.parse({
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configuration: {
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provider: 'openai-compatible',
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model: settings.knowledgeEmbeddingModel,
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endpoint: settings.knowledgeEmbeddingBaseUrl,
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credentialConfigured:
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settings.knowledgeEmbeddingApiKeyConfigured
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}
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})
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return getEmbeddingSettingsSnapshot()
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})
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registerHandler(ipcChannels.embeddingDiagnose, async (event) => {
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assertTrustedSender(event, window)
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return diagnoseEmbeddingProvider(
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await requireEmbeddingProvider()
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)
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})
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registerHandler(
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ipcChannels.embeddingDiagnose,
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async (event, input: unknown) => {
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assertTrustedSender(event, window)
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const { connectionId } =
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embeddingConnectionIdRequestSchema.parse(input)
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const provider = await requireEmbeddingProvider(connectionId)
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try {
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return await diagnoseEmbeddingProvider(provider)
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} finally {
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await (
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provider as EmbeddingProvider & {
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dispose?: () => void | Promise<void>
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}
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).dispose?.()
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}
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}
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)
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registerHandler(
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ipcChannels.embeddingSetCurrent,
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async (event, input: unknown) => {
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assertTrustedSender(event, window)
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if (!setCurrentEmbeddingConnection) {
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throw new Error('向量模型设置服务不可用')
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}
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const { connectionId } =
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embeddingConnectionIdRequestSchema.parse(input)
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await setCurrentEmbeddingConnection(connectionId)
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return getEmbeddingSettingsSnapshot()
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}
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)
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registerHandler(
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ipcChannels.embeddingModelsInstall,
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async (event, input: unknown) => {
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assertTrustedSender(event, window)
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if (!embeddingModelManager || !applicationSettingsStore) {
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throw new Error('内置向量模型服务不可用')
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}
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const { modelId, expectedDownloadSource } =
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embeddingModelInstallInputSchema.parse(input)
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const { modelDownloadSource } =
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await applicationSettingsStore.get()
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if (modelDownloadSource !== expectedDownloadSource) {
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throw new Error('模型下载源已变化，请刷新后重试')
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}
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await embeddingModelManager.install(
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modelId,
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expectedDownloadSource
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)
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return embeddingModelSnapshotSchema.parse(
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await embeddingModelManager.getSnapshot()
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)
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}
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)
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registerHandler(
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ipcChannels.embeddingModelsCancel,
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(event, input: unknown) => {
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assertTrustedSender(event, window)
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if (!embeddingModelManager) {
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throw new Error('内置向量模型服务不可用')
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}
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const { modelId } =
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embeddingModelActionInputSchema.parse(input)
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return embeddingModelManager.cancel(modelId)
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}
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)
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registerHandler(
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ipcChannels.embeddingModelsImportArchive,
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async (event, input: unknown) => {
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assertTrustedSender(event, window)
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if (!embeddingModelManager?.importArchive) {
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throw new Error('当前版本不支持导入向量模型 ZIP')
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}
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const { modelId } =
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embeddingModelActionInputSchema.parse(input)
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const result = await dialog.showOpenDialog(window, {
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title: '导入向量模型 ZIP',
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properties: ['openFile'],
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filters: modelArchiveDialogFilters
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})
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const archivePath = result.filePaths[0]
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if (result.canceled || !archivePath) {
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return undefined
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}
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await embeddingModelManager.importArchive(
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modelId,
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archivePath
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)
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return embeddingModelSnapshotSchema.parse(
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await embeddingModelManager.getSnapshot()
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)
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}
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)
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registerHandler(
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ipcChannels.embeddingModelsRemove,
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async (event, input: unknown) => {
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assertTrustedSender(event, window)
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if (!embeddingModelManager) {
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throw new Error('内置向量模型服务不可用')
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}
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const { modelId } =
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embeddingModelActionInputSchema.parse(input)
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await embeddingModelManager.remove(modelId)
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return embeddingModelSnapshotSchema.parse(
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await embeddingModelManager.getSnapshot()
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)
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}
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)
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registerHandler(ipcChannels.speechModelsGet, (event) => {
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assertTrustedSender(event, window)
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@@ -6091,13 +6279,28 @@ export function registerIpcHandlers(
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const embeddingConfiguration = async () => {
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const settings = await settingsStore.getPublicSettings()
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return {
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provider: 'openai-compatible',
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model: settings.knowledgeEmbeddingModel,
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endpoint: settings.knowledgeEmbeddingBaseUrl,
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credentialConfigured:
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settings.knowledgeEmbeddingApiKeyConfigured
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}
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const connection = settings.embeddingConnections?.find(
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(candidate) =>
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candidate.id === settings.activeEmbeddingConnectionId
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)
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return connection?.kind === 'builtin'
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? {
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provider: 'builtin',
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model: 'granite-embedding-97m-multilingual-r2',
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credentialConfigured: false
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}
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: {
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provider: 'openai-compatible',
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model:
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connection?.modelName ??
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settings.knowledgeEmbeddingModel,
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endpoint:
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connection?.baseUrl ??
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settings.knowledgeEmbeddingBaseUrl,
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credentialConfigured:
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connection?.apiKeyConfigured ??
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settings.knowledgeEmbeddingApiKeyConfigured
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}
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}
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registerHandler(
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