feat: add LM Studio error messages and validation handling in embedder

src/i18n/locales/ca/embeddings.json

@@ -17,6 +17,11 @@
 		"modelNotEmbeddingCapable": "El model d'Ollama no és capaç de fer incrustacions: {{modelId}}",
 		"hostNotFound": "No s'ha trobat l'amfitrió d'Ollama: {{baseUrl}}"
 	},
+	"lmstudio": {
+		"serviceNotRunning": "El servidor de LM Studio no s'està executant a {{baseUrl}}. Assegureu-vos que LM Studio s'estigui executant amb el servidor local activat.",
+		"modelNotFound": "No s'ha trobat el model \"{{modelId}}\" a LM Studio. Assegureu-vos que el model estigui carregat a LM Studio.",
+		"hostNotFound": "No es pot connectar a LM Studio a {{baseUrl}}. Comproveu la configuració de l'URL base."
+	},
 	"scanner": {
 		"unknownErrorProcessingFile": "Error desconegut en processar el fitxer {{filePath}}",
 		"unknownErrorDeletingPoints": "Error desconegut en eliminar els punts per a {{filePath}}",

src/i18n/locales/de/embeddings.json

@@ -17,6 +17,11 @@
 		"modelNotEmbeddingCapable": "Ollama-Modell ist nicht für Einbettungen geeignet: {{modelId}}",
 		"hostNotFound": "Ollama-Host nicht gefunden: {{baseUrl}}"
 	},
+	"lmstudio": {
+		"serviceNotRunning": "Der LM Studio-Server wird unter {{baseUrl}} nicht ausgeführt. Bitte stelle sicher, dass LM Studio mit aktiviertem lokalen Server läuft.",
+		"modelNotFound": "Modell \"{{modelId}}\" in LM Studio nicht gefunden. Bitte stelle sicher, dass das Modell in LM Studio geladen ist.",
+		"hostNotFound": "Verbindung zu LM Studio unter {{baseUrl}} nicht möglich. Bitte überprüfe die Konfiguration der Basis-URL."
+	},
 	"scanner": {
 		"unknownErrorProcessingFile": "Unbekannter Fehler beim Verarbeiten der Datei {{filePath}}",
 		"unknownErrorDeletingPoints": "Unbekannter Fehler beim Löschen der Punkte für {{filePath}}",

src/i18n/locales/en/embeddings.json

@@ -17,6 +17,11 @@
 		"modelNotEmbeddingCapable": "Ollama model is not embedding capable: {{modelId}}",
 		"hostNotFound": "Ollama host not found: {{baseUrl}}"
 	},
+	"lmstudio": {
+		"serviceNotRunning": "LM Studio server is not running at {{baseUrl}}. Please ensure LM Studio is running with the local server enabled.",
+		"modelNotFound": "Model \"{{modelId}}\" not found in LM Studio. Please ensure the model is loaded in LM Studio.",
+		"hostNotFound": "Cannot connect to LM Studio at {{baseUrl}}. Please check the base URL configuration."
+	},
 	"scanner": {
 		"unknownErrorProcessingFile": "Unknown error processing file {{filePath}}",
 		"unknownErrorDeletingPoints": "Unknown error deleting points for {{filePath}}",

src/i18n/locales/es/embeddings.json

@@ -17,6 +17,11 @@
 		"modelNotEmbeddingCapable": "El modelo Ollama no es capaz de realizar incrustaciones: {{modelId}}",
 		"hostNotFound": "No se encuentra el host de Ollama: {{baseUrl}}"
 	},
+	"lmstudio": {
+		"serviceNotRunning": "El servidor de LM Studio no se está ejecutando en {{baseUrl}}. Asegúrate de que LM Studio se esté ejecutando con el servidor local habilitado.",
+		"modelNotFound": "No se encontró el modelo \"{{modelId}}\" en LM Studio. Asegúrate de que el modelo esté cargado en LM Studio.",
+		"hostNotFound": "No se puede conectar a LM Studio en {{baseUrl}}. Comprueba la configuración de la URL base."
+	},
 	"scanner": {
 		"unknownErrorProcessingFile": "Error desconocido procesando archivo {{filePath}}",
 		"unknownErrorDeletingPoints": "Error desconocido eliminando puntos para {{filePath}}",

src/i18n/locales/fr/embeddings.json

@@ -17,6 +17,11 @@
 		"modelNotEmbeddingCapable": "Le modèle Ollama n'est pas capable d'intégrer : {{modelId}}",
 		"hostNotFound": "Hôte Ollama introuvable : {{baseUrl}}"
 	},
+	"lmstudio": {
+		"serviceNotRunning": "Le serveur LM Studio n'est pas en cours d'exécution à {{baseUrl}}. Veuillez vous assurer que LM Studio est en cours d'exécution avec le serveur local activé.",
+		"modelNotFound": "Le modèle \"{{modelId}}\" n'a pas été trouvé dans LM Studio. Veuillez vous assurer que le modèle est chargé dans LM Studio.",
+		"hostNotFound": "Impossible de se connecter à LM Studio à {{baseUrl}}. Veuillez vérifier la configuration de l'URL de base."
+	},
 	"scanner": {
 		"unknownErrorProcessingFile": "Erreur inconnue lors du traitement du fichier {{filePath}}",
 		"unknownErrorDeletingPoints": "Erreur inconnue lors de la suppression des points pour {{filePath}}",

src/i18n/locales/hi/embeddings.json

@@ -17,6 +17,11 @@
 		"modelNotEmbeddingCapable": "ओलामा मॉडल एम्बेडिंग में सक्षम नहीं है: {{modelId}}",
 		"hostNotFound": "ओलामा होस्ट नहीं मिला: {{baseUrl}}"
 	},
+	"lmstudio": {
+		"serviceNotRunning": "एलएम स्टूडियो सर्वर {{baseUrl}} पर नहीं चल रहा है। कृपया सुनिश्चित करें कि एलएम स्टूडियो स्थानीय सर्वर सक्षम के साथ चल रहा है।",
+		"modelNotFound": "एलएम स्टूडियो में मॉडल \"{{modelId}}\" नहीं मिला। कृपया सुनिश्चित करें कि मॉडल एलएम स्टूडियो में लोड किया गया है।",
+		"hostNotFound": "{{baseUrl}} पर एलएम स्टूडियो से कनेक्ट नहीं हो सकता। कृपया आधार यूआरएल कॉन्फ़िगरेशन की जांच करें।"
+	},
 	"scanner": {
 		"unknownErrorProcessingFile": "फ़ाइल {{filePath}} प्रसंस्करण में अज्ञात त्रुटि",
 		"unknownErrorDeletingPoints": "{{filePath}} के लिए बिंदु हटाने में अज्ञात त्रुटि",

src/i18n/locales/id/embeddings.json

@@ -17,6 +17,11 @@
 		"modelNotEmbeddingCapable": "Model Ollama tidak mampu melakukan embedding: {{modelId}}",
 		"hostNotFound": "Host Ollama tidak ditemukan: {{baseUrl}}"
 	},
+	"lmstudio": {
+		"serviceNotRunning": "Server LM Studio tidak berjalan di {{baseUrl}}. Pastikan LM Studio berjalan dengan server lokal diaktifkan.",
+		"modelNotFound": "Model \"{{modelId}}\" tidak ditemukan di LM Studio. Pastikan model dimuat di LM Studio.",
+		"hostNotFound": "Tidak dapat terhubung ke LM Studio di {{baseUrl}}. Silakan periksa konfigurasi URL dasar."
+	},
 	"scanner": {
 		"unknownErrorProcessingFile": "Error tidak dikenal saat memproses file {{filePath}}",
 		"unknownErrorDeletingPoints": "Error tidak dikenal saat menghapus points untuk {{filePath}}",

src/i18n/locales/it/embeddings.json

@@ -17,6 +17,11 @@
 		"modelNotEmbeddingCapable": "Il modello Ollama non è in grado di eseguire l'embedding: {{modelId}}",
 		"hostNotFound": "Host Ollama non trovato: {{baseUrl}}"
 	},
+	"lmstudio": {
+		"serviceNotRunning": "Il server di LM Studio non è in esecuzione su {{baseUrl}}. Assicurati che LM Studio sia in esecuzione con il server locale abilitato.",
+		"modelNotFound": "Modello \"{{modelId}}\" non trovato in LM Studio. Assicurati che il modello sia caricato in LM Studio.",
+		"hostNotFound": "Impossibile connettersi a LM Studio su {{baseUrl}}. Controlla la configurazione dell'URL di base."
+	},
 	"scanner": {
 		"unknownErrorProcessingFile": "Errore sconosciuto nell'elaborazione del file {{filePath}}",
 		"unknownErrorDeletingPoints": "Errore sconosciuto nell'eliminazione dei punti per {{filePath}}",

src/i18n/locales/ja/embeddings.json

@@ -17,6 +17,11 @@
 		"modelNotEmbeddingCapable": "Ollamaモデルは埋め込みに対応していません：{{modelId}}",
 		"hostNotFound": "Ollamaホストが見つかりません：{{baseUrl}}"
 	},
+	"lmstudio": {
+		"serviceNotRunning": "LM Studioサーバーが{{baseUrl}}で実行されていません。LM Studioがローカルサーバーを有効にして実行されていることを確認してください。",
+		"modelNotFound": "モデル「{{modelId}}」がLM Studioで見つかりません。モデルがLM Studioにロードされていることを確認してください。",
+		"hostNotFound": "{{baseUrl}}のLM Studioに接続できません。ベースURLの構成を確認してください。"
+	},
 	"scanner": {
 		"unknownErrorProcessingFile": "ファイル{{filePath}}の処理中に不明なエラーが発生しました",
 		"unknownErrorDeletingPoints": "{{filePath}}のポイント削除中に不明なエラーが発生しました",

src/i18n/locales/ko/embeddings.json

@@ -17,6 +17,11 @@
 		"modelNotEmbeddingCapable": "Ollama 모델은 임베딩이 불가능합니다: {{modelId}}",
 		"hostNotFound": "Ollama 호스트를 찾을 수 없습니다: {{baseUrl}}"
 	},
+	"lmstudio": {
+		"serviceNotRunning": "LM Studio 서버가 {{baseUrl}}에서 실행되고 있지 않습니다. 로컬 서버가 활성화된 상태로 LM Studio가 실행 중인지 확인하세요.",
+		"modelNotFound": "LM Studio에서 \"{{modelId}}\" 모델을 찾을 수 없습니다. 모델이 LM Studio에 로드되었는지 확인하세요.",
+		"hostNotFound": "{{baseUrl}}에서 LM Studio에 연결할 수 없습니다. 기본 URL 구성을 확인하세요."
+	},
 	"scanner": {
 		"unknownErrorProcessingFile": "파일 {{filePath}} 처리 중 알 수 없는 오류",
 		"unknownErrorDeletingPoints": "{{filePath}}의 포인트 삭제 중 알 수 없는 오류",

src/i18n/locales/nl/embeddings.json

@@ -17,6 +17,11 @@
 		"modelNotEmbeddingCapable": "Ollama-model is niet in staat tot insluiten: {{modelId}}",
 		"hostNotFound": "Ollama-host niet gevonden: {{baseUrl}}"
 	},
+	"lmstudio": {
+		"serviceNotRunning": "LM Studio-server draait niet op {{baseUrl}}. Zorg ervoor dat LM Studio draait met de lokale server ingeschakeld.",
+		"modelNotFound": "Model \"{{modelId}}\" niet gevonden in LM Studio. Zorg ervoor dat het model in LM Studio is geladen.",
+		"hostNotFound": "Kan geen verbinding maken met LM Studio op {{baseUrl}}. Controleer de basis-URL-configuratie."
+	},
 	"scanner": {
 		"unknownErrorProcessingFile": "Onbekende fout bij verwerken van bestand {{filePath}}",
 		"unknownErrorDeletingPoints": "Onbekende fout bij verwijderen van punten voor {{filePath}}",

src/i18n/locales/pl/embeddings.json

@@ -17,6 +17,11 @@
 		"modelNotEmbeddingCapable": "Model Ollama nie jest zdolny do osadzania: {{modelId}}",
 		"hostNotFound": "Nie znaleziono hosta Ollama: {{baseUrl}}"
 	},
+	"lmstudio": {
+		"serviceNotRunning": "Serwer LM Studio nie działa pod adresem {{baseUrl}}. Upewnij się, że LM Studio jest uruchomione z włączonym serwerem lokalnym.",
+		"modelNotFound": "Nie znaleziono modelu \"{{modelId}}\" w LM Studio. Upewnij się, że model jest załadowany w LM Studio.",
+		"hostNotFound": "Nie można połączyć się z LM Studio pod adresem {{baseUrl}}. Sprawdź konfigurację podstawowego adresu URL."
+	},
 	"scanner": {
 		"unknownErrorProcessingFile": "Nieznany błąd podczas przetwarzania pliku {{filePath}}",
 		"unknownErrorDeletingPoints": "Nieznany błąd podczas usuwania punktów dla {{filePath}}",

src/i18n/locales/pt-BR/embeddings.json

@@ -17,6 +17,11 @@
 		"modelNotEmbeddingCapable": "O modelo Ollama não é capaz de embedding: {{modelId}}",
 		"hostNotFound": "Host Ollama não encontrado: {{baseUrl}}"
 	},
+	"lmstudio": {
+		"serviceNotRunning": "O servidor do LM Studio não está em execução em {{baseUrl}}. Certifique-se de que o LM Studio esteja em execução com o servidor local ativado.",
+		"modelNotFound": "Modelo \"{{modelId}}\" não encontrado no LM Studio. Certifique-se de que o modelo esteja carregado no LM Studio.",
+		"hostNotFound": "Não é possível conectar-se ao LM Studio em {{baseUrl}}. Verifique a configuração do URL base."
+	},
 	"scanner": {
 		"unknownErrorProcessingFile": "Erro desconhecido ao processar arquivo {{filePath}}",
 		"unknownErrorDeletingPoints": "Erro desconhecido ao deletar pontos para {{filePath}}",

src/i18n/locales/ru/embeddings.json

@@ -17,6 +17,11 @@
 		"modelNotEmbeddingCapable": "Модель Ollama не способна к вложению: {{modelId}}",
 		"hostNotFound": "Хост Ollama не найден: {{baseUrl}}"
 	},
+	"lmstudio": {
+		"serviceNotRunning": "Сервер LM Studio не запущен по адресу {{baseUrl}}. Убедитесь, что LM Studio запущен с включенным локальным сервером.",
+		"modelNotFound": "Модель \"{{modelId}}\" не найдена в LM Studio. Убедитесь, что модель загружена в LM Studio.",
+		"hostNotFound": "Не удается подключиться к LM Studio по адресу {{baseUrl}}. Проверьте конфигурацию базового URL-адреса."
+	},
 	"scanner": {
 		"unknownErrorProcessingFile": "Неизвестная ошибка при обработке файла {{filePath}}",
 		"unknownErrorDeletingPoints": "Неизвестная ошибка при удалении точек для {{filePath}}",

src/i18n/locales/tr/embeddings.json

@@ -17,6 +17,11 @@
 		"modelNotEmbeddingCapable": "Ollama modeli gömme yeteneğine sahip değil: {{modelId}}",
 		"hostNotFound": "Ollama ana bilgisayarı bulunamadı: {{baseUrl}}"
 	},
+	"lmstudio": {
+		"serviceNotRunning": "LM Studio sunucusu {{baseUrl}} adresinde çalışmıyor. Lütfen LM Studio'nun yerel sunucu etkinken çalıştığından emin olun.",
+		"modelNotFound": "LM Studio'da \"{{modelId}}\" modeli bulunamadı. Lütfen modelin LM Studio'da yüklü olduğundan emin olun.",
+		"hostNotFound": "LM Studio'ya {{baseUrl}} adresinden bağlanılamıyor. Lütfen temel URL yapılandırmasını kontrol edin."
+	},
 	"scanner": {
 		"unknownErrorProcessingFile": "{{filePath}} dosyası işlenirken bilinmeyen hata",
 		"unknownErrorDeletingPoints": "{{filePath}} için noktalar silinirken bilinmeyen hata",

src/i18n/locales/vi/embeddings.json

@@ -17,6 +17,11 @@
 		"modelNotEmbeddingCapable": "Mô hình Ollama không có khả năng nhúng: {{modelId}}",
 		"hostNotFound": "Không tìm thấy máy chủ Ollama: {{baseUrl}}"
 	},
+	"lmstudio": {
+		"serviceNotRunning": "Máy chủ LM Studio không chạy tại {{baseUrl}}. Vui lòng đảm bảo LM Studio đang chạy với máy chủ cục bộ được bật.",
+		"modelNotFound": "Không tìm thấy mô hình \"{{modelId}}\" trong LM Studio. Vui lòng đảm bảo mô hình đã được tải trong LM Studio.",
+		"hostNotFound": "Không thể kết nối với LM Studio tại {{baseUrl}}. Vui lòng kiểm tra cấu hình URL cơ sở."
+	},
 	"scanner": {
 		"unknownErrorProcessingFile": "Lỗi không xác định khi xử lý tệp {{filePath}}",
 		"unknownErrorDeletingPoints": "Lỗi không xác định khi xóa điểm cho {{filePath}}",

src/i18n/locales/zh-CN/embeddings.json

@@ -17,6 +17,11 @@
 		"modelNotEmbeddingCapable": "Ollama 模型不具备嵌入能力：{{modelId}}",
 		"hostNotFound": "未找到 Ollama 主机：{{baseUrl}}"
 	},
+	"lmstudio": {
+		"serviceNotRunning": "LM Studio 服务器未在 {{baseUrl}} 运行。请确保 LM Studio 已启用本地服务器并正在运行。",
+		"modelNotFound": "在 LM Studio 中未找到模型“{{modelId}}”。请确保该模型已在 LM Studio 中加载。",
+		"hostNotFound": "无法连接到 {{baseUrl}} 上的 LM Studio。请检查基本 URL 配置。"
+	},
 	"scanner": {
 		"unknownErrorProcessingFile": "处理文件 {{filePath}} 时出现未知错误",
 		"unknownErrorDeletingPoints": "删除 {{filePath}} 的数据点时出现未知错误",

src/i18n/locales/zh-TW/embeddings.json

@@ -17,6 +17,11 @@
 		"modelNotEmbeddingCapable": "Ollama 模型不具備內嵌能力：{{modelId}}",
 		"hostNotFound": "找不到 Ollama 主機：{{baseUrl}}"
 	},
+	"lmstudio": {
+		"serviceNotRunning": "LM Studio 伺服器未在 {{baseUrl}} 執行。請確保 LM Studio 已啟用本機伺服器並正在執行。",
+		"modelNotFound": "在 LM Studio 中找不到模型「{{modelId}}」。請確保該模型已在 LM Studio 中載入。",
+		"hostNotFound": "無法連線至 {{baseUrl}} 上的 LM Studio。請檢查基礎 URL 設定。"
+	},
 	"scanner": {
 		"unknownErrorProcessingFile": "處理檔案 {{filePath}} 時發生未知錯誤",
 		"unknownErrorDeletingPoints": "刪除 {{filePath}} 的資料點時發生未知錯誤",

src/services/code-index/embedders/__tests__/lmstudio.spec.ts

@@ -0,0 +1,210 @@
+import { vitest, describe, it, expect, beforeEach, afterEach } from "vitest"
+import type { MockedFunction } from "vitest"
+import { CodeIndexLmStudioEmbedder } from "../lmstudio"
+import { OpenAI } from "openai"
+
+vitest.mock("openai", () => {
+	const mockEmbeddingsCreate = vitest.fn()
+	return {
+		OpenAI: vitest.fn().mockImplementation(() => ({
+			embeddings: {
+				create: mockEmbeddingsCreate,
+			},
+		})),
+	}
+})
+
+const consoleMocks = {
+	error: vitest.spyOn(console, "error").mockImplementation(() => {}),
+	warn: vitest.spyOn(console, "warn").mockImplementation(() => {}),
+}
+
+describe("CodeIndexLmStudioEmbedder", () => {
+	let embedder: CodeIndexLmStudioEmbedder
+	let mockEmbeddingsCreate: MockedFunction<any>
+
+	beforeEach(() => {
+		vitest.clearAllMocks()
+		consoleMocks.error.mockClear()
+		consoleMocks.warn.mockClear()
+
+		const MockedOpenAI = OpenAI as any
+		mockEmbeddingsCreate = vitest.fn()
+		MockedOpenAI.mockImplementation(() => ({
+			embeddings: {
+				create: mockEmbeddingsCreate,
+			},
+		}))
+
+		embedder = new CodeIndexLmStudioEmbedder({
+			lmStudioBaseUrl: "http://localhost:1234",
+			embeddingModelId: "text-embedding-nomic-embed-text-v1.5@f16",
+		})
+	})
+
+	afterEach(() => {
+		vitest.clearAllMocks()
+	})
+
+	describe("constructor", () => {
+		it("should initialize with provided options", () => {
+			expect(embedder.embedderInfo.name).toBe("lmstudio")
+		})
+
+		it("should use default values when not provided", () => {
+			const embedderWithDefaults = new CodeIndexLmStudioEmbedder({})
+			expect(embedderWithDefaults.embedderInfo.name).toBe("lmstudio")
+		})
+
+		it("should normalize base URL to include /v1", () => {
+			const embedderWithoutV1 = new CodeIndexLmStudioEmbedder({
+				lmStudioBaseUrl: "http://localhost:1234",
+			})
+			expect(embedderWithoutV1.embedderInfo.name).toBe("lmstudio")
+
+			const embedderWithV1 = new CodeIndexLmStudioEmbedder({
+				lmStudioBaseUrl: "http://localhost:1234/v1",
+			})
+			expect(embedderWithV1.embedderInfo.name).toBe("lmstudio")
+		})
+	})
+
+	describe("validateConfiguration", () => {
+		it("should validate successfully with valid configuration", async () => {
+			const mockResponse = {
+				data: [{ embedding: [0.1, 0.2, 0.3] }],
+				usage: { prompt_tokens: 2, total_tokens: 2 },
+			}
+			mockEmbeddingsCreate.mockResolvedValue(mockResponse)
+
+			const result = await embedder.validateConfiguration()
+
+			expect(result.valid).toBe(true)
+			expect(result.error).toBeUndefined()
+			expect(mockEmbeddingsCreate).toHaveBeenCalledWith({
+				input: ["test"],
+				model: "text-embedding-nomic-embed-text-v1.5@f16",
+				encoding_format: "float",
+			})
+		})
+
+		it("should fail validation when response has no data", async () => {
+			const mockResponse = {
+				data: [],
+				usage: { prompt_tokens: 0, total_tokens: 0 },
+			}
+			mockEmbeddingsCreate.mockResolvedValue(mockResponse)
+
+			const result = await embedder.validateConfiguration()
+
+			expect(result.valid).toBe(false)
+			expect(result.error).toBe("embeddings:validation.invalidResponse")
+		})
+
+		it("should fail validation when LM Studio is not running (ECONNREFUSED)", async () => {
+			const error = new Error("ECONNREFUSED")
+			;(error as any).code = "ECONNREFUSED"
+			mockEmbeddingsCreate.mockRejectedValue(error)
+
+			const result = await embedder.validateConfiguration()
+
+			expect(result.valid).toBe(false)
+			expect(result.error).toBe("lmstudio.serviceNotRunning")
+		})
+
+		it("should fail validation when model is not found (404)", async () => {
+			const error = new Error("HTTP 404: Not Found")
+			;(error as any).status = 404
+			mockEmbeddingsCreate.mockRejectedValue(error)
+
+			const result = await embedder.validateConfiguration()
+
+			expect(result.valid).toBe(false)
+			expect(result.error).toBe("lmstudio.modelNotFound")
+		})
+
+		it("should fail validation when host is not found (ENOTFOUND)", async () => {
+			const error = new Error("ENOTFOUND")
+			;(error as any).code = "ENOTFOUND"
+			mockEmbeddingsCreate.mockRejectedValue(error)
+
+			const result = await embedder.validateConfiguration()
+
+			expect(result.valid).toBe(false)
+			expect(result.error).toBe("lmstudio.hostNotFound")
+		})
+
+		it("should handle generic errors with standard error handling", async () => {
+			const error = new Error("Unknown error")
+			mockEmbeddingsCreate.mockRejectedValue(error)
+
+			const result = await embedder.validateConfiguration()
+
+			expect(result.valid).toBe(false)
+
+			expect(result.error).toBe("embeddings:validation.configurationError")
+		})
+
+		it("should handle fetch failed errors", async () => {
+			const error = new Error("fetch failed")
+			mockEmbeddingsCreate.mockRejectedValue(error)
+
+			const result = await embedder.validateConfiguration()
+
+			expect(result.valid).toBe(false)
+			expect(result.error).toBe("lmstudio.serviceNotRunning")
+		})
+	})
+
+	describe("createEmbeddings", () => {
+		it("should create embeddings successfully", async () => {
+			const mockResponse = {
+				data: [{ embedding: [0.1, 0.2, 0.3] }, { embedding: [0.4, 0.5, 0.6] }],
+				usage: { prompt_tokens: 10, total_tokens: 10 },
+			}
+			mockEmbeddingsCreate.mockResolvedValue(mockResponse)
+
+			const result = await embedder.createEmbeddings(["test1", "test2"])
+
+			expect(result.embeddings).toEqual([
+				[0.1, 0.2, 0.3],
+				[0.4, 0.5, 0.6],
+			])
+			expect(result.usage).toEqual({ promptTokens: 10, totalTokens: 10 })
+		})
+
+		it("should handle rate limit errors with retry", async () => {
+			const error = new Error("Rate limit exceeded")
+			;(error as any).status = 429
+
+			mockEmbeddingsCreate.mockRejectedValueOnce(error)
+
+			const mockResponse = {
+				data: [{ embedding: [0.1, 0.2, 0.3] }],
+				usage: { prompt_tokens: 5, total_tokens: 5 },
+			}
+			mockEmbeddingsCreate.mockResolvedValueOnce(mockResponse)
+
+			const result = await embedder.createEmbeddings(["test"])
+
+			expect(result.embeddings).toEqual([[0.1, 0.2, 0.3]])
+			expect(mockEmbeddingsCreate).toHaveBeenCalledTimes(2)
+		})
+
+		it("should skip texts that exceed token limit", async () => {
+			const longText = "a".repeat(100000)
+			const shortText = "short text"
+
+			const mockResponse = {
+				data: [{ embedding: [0.1, 0.2, 0.3] }],
+				usage: { prompt_tokens: 3, total_tokens: 3 },
+			}
+			mockEmbeddingsCreate.mockResolvedValue(mockResponse)
+
+			const result = await embedder.createEmbeddings([longText, shortText])
+
+			expect(result.embeddings).toEqual([[0.1, 0.2, 0.3]])
+			expect(consoleMocks.warn).toHaveBeenCalledWith(expect.stringContaining("exceeds maximum token limit"))
+		})
+	})
+})

src/services/code-index/embedders/lmstudio.ts

@@ -7,6 +7,8 @@ import {
 	MAX_BATCH_RETRIES as MAX_RETRIES,
 	INITIAL_RETRY_DELAY_MS as INITIAL_DELAY_MS,
 } from "../constants"
+import { withValidationErrorHandling, formatEmbeddingError, HttpError } from "../shared/validation-helpers"
+import { t } from "../../../i18n"
 
 /**
  * LM Studio implementation of the embedder interface with batching and rate limiting.
@@ -81,19 +83,10 @@ export class CodeIndexLmStudioEmbedder implements IEmbedder {
 			}
 
 			if (currentBatch.length > 0) {
-				try {
-					const batchResult = await this._embedBatchWithRetries(currentBatch, modelToUse)
-
-					allEmbeddings.push(...batchResult.embeddings)
-					usage.promptTokens += batchResult.usage.promptTokens
-					usage.totalTokens += batchResult.usage.totalTokens
-				} catch (error) {
-					const batchInfo = `batch of ${currentBatch.length} documents (indices: ${processedIndices.join(", ")})`
-					console.error(`Failed to process ${batchInfo}:`, error)
-					throw new Error(
-						`Failed to create embeddings for ${batchInfo}: ${error instanceof Error ? error.message : "batch processing error"}`,
-					)
-				}
+				const batchResult = await this._embedBatchWithRetries(currentBatch, modelToUse)
+				allEmbeddings.push(...batchResult.embeddings)
+				usage.promptTokens += batchResult.usage.promptTokens
+				usage.totalTokens += batchResult.usage.totalTokens
 			}
 		}
 
@@ -115,7 +108,6 @@ export class CodeIndexLmStudioEmbedder implements IEmbedder {
 				const response = await this.embeddingsClient.embeddings.create({
 					input: batchTexts,
 					model: model,
-					encoding_format: "float",
 				})
 
 				return {
@@ -126,10 +118,11 @@ export class CodeIndexLmStudioEmbedder implements IEmbedder {
 					},
 				}
 			} catch (error: any) {
-				const isRateLimitError = error?.status === 429
 				const hasMoreAttempts = attempts < MAX_RETRIES - 1
 
-				if (isRateLimitError && hasMoreAttempts) {
+				// Check if it's a rate limit error
+				const httpError = error as HttpError
+				if (httpError?.status === 429 && hasMoreAttempts) {
 					const delayMs = INITIAL_DELAY_MS * Math.pow(2, attempts)
 					await new Promise((resolve) => setTimeout(resolve, delayMs))
 					continue
@@ -139,7 +132,86 @@ export class CodeIndexLmStudioEmbedder implements IEmbedder {
 			}
 		}
 
-		throw new Error(`Failed to create embeddings after ${MAX_RETRIES} attempts`)
+		throw new Error(t("embeddings:failedMaxAttempts", { attempts: MAX_RETRIES }))
+	}
+
+	/**
+	 * Validates the LM Studio embedder configuration by testing connectivity and model availability
+	 * @returns Promise resolving to validation result with success status and optional error message
+	 */
+	async validateConfiguration(): Promise<{ valid: boolean; error?: string }> {
+		return withValidationErrorHandling(
+			async () => {
+				// Test with a minimal embedding request
+				const testTexts = ["test"]
+				const modelToUse = this.defaultModelId
+
+				try {
+					const response = await this.embeddingsClient.embeddings.create({
+						input: testTexts,
+						model: modelToUse,
+					})
+
+					// Check if we got a valid response
+					if (!response.data || response.data.length === 0) {
+						return {
+							valid: false,
+							error: t("embeddings:validation.invalidResponse"),
+						}
+					}
+
+					return { valid: true }
+				} catch (error: any) {
+					// Handle LM Studio specific errors
+					if (error?.message?.includes("ECONNREFUSED") || error?.code === "ECONNREFUSED") {
+						return {
+							valid: false,
+							error: t("embeddings:lmstudio.serviceNotRunning", {
+								baseUrl: this.options.lmStudioBaseUrl,
+							}),
+						}
+					}
+
+					if (error?.status === 404 || error?.message?.includes("404")) {
+						return {
+							valid: false,
+							error: t("embeddings:lmstudio.modelNotFound", { modelId: modelToUse }),
+						}
+					}
+
+					// Re-throw to let standard error handling take over
+					throw error
+				}
+			},
+			"lmstudio",
+			{
+				beforeStandardHandling: (error: any) => {
+					// Handle LM Studio-specific connection errors
+					if (
+						error?.message?.includes("fetch failed") ||
+						error?.code === "ECONNREFUSED" ||
+						error?.message?.includes("ECONNREFUSED")
+					) {
+						return {
+							valid: false,
+							error: t("embeddings:lmstudio.serviceNotRunning", {
+								baseUrl: this.options.lmStudioBaseUrl,
+							}),
+						}
+					}
+
+					if (error?.code === "ENOTFOUND" || error?.message?.includes("ENOTFOUND")) {
+						return {
+							valid: false,
+							error: t("embeddings:lmstudio.hostNotFound", { baseUrl: this.options.lmStudioBaseUrl }),
+						}
+					}
+
+					// Let standard handling take over
+					return undefined
+				},
+			},
+		)
 	}
 
 	get embedderInfo(): EmbedderInfo {