fix(code-index): remove deprecated text-embedding-004 and migrate to gemini-embedding-001

src/services/code-index/__tests__/service-factory.spec.ts

@@ -286,7 +286,7 @@ describe("CodeIndexServiceFactory", () => {
 			// Arrange
 			const testConfig = {
 				embedderProvider: "gemini",
-				modelId: "text-embedding-004",
+				modelId: "gemini-embedding-001",
 				geminiOptions: {
 					apiKey: "test-gemini-api-key",
 				},
@@ -297,6 +297,25 @@ describe("CodeIndexServiceFactory", () => {
 			factory.createEmbedder()
 
 			// Assert
+			expect(MockedGeminiEmbedder).toHaveBeenCalledWith("test-gemini-api-key", "gemini-embedding-001")
+		})
+
+		it("should pass deprecated text-embedding-004 modelId to GeminiEmbedder (migration happens inside GeminiEmbedder)", () => {
+			// Arrange - service-factory passes the config modelId directly;
+			// GeminiEmbedder handles the migration internally
+			const testConfig = {
+				embedderProvider: "gemini",
+				modelId: "text-embedding-004",
+				geminiOptions: {
+					apiKey: "test-gemini-api-key",
+				},
+			}
+			mockConfigManager.getConfig.mockReturnValue(testConfig as any)
+
+			// Act
+			factory.createEmbedder()
+
+			// Assert - factory passes the original modelId; GeminiEmbedder migrates it internally
 			expect(MockedGeminiEmbedder).toHaveBeenCalledWith("test-gemini-api-key", "text-embedding-004")
 		})
 

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

@@ -44,7 +44,7 @@ describe("GeminiEmbedder", () => {
 		it("should create an instance with specified model", () => {
 			// Arrange
 			const apiKey = "test-gemini-api-key"
-			const modelId = "text-embedding-004"
+			const modelId = "gemini-embedding-001"
 
 			// Act
 			embedder = new GeminiEmbedder(apiKey, modelId)
@@ -53,7 +53,24 @@ describe("GeminiEmbedder", () => {
 			expect(MockedOpenAICompatibleEmbedder).toHaveBeenCalledWith(
 				"https://generativelanguage.googleapis.com/v1beta/openai/",
 				apiKey,
-				"text-embedding-004",
+				"gemini-embedding-001",
+				2048,
+			)
+		})
+
+		it("should migrate deprecated text-embedding-004 to gemini-embedding-001", () => {
+			// Arrange
+			const apiKey = "test-gemini-api-key"
+			const deprecatedModelId = "text-embedding-004"
+
+			// Act
+			embedder = new GeminiEmbedder(apiKey, deprecatedModelId)
+
+			// Assert - should be migrated to gemini-embedding-001
+			expect(MockedOpenAICompatibleEmbedder).toHaveBeenCalledWith(
+				"https://generativelanguage.googleapis.com/v1beta/openai/",
+				apiKey,
+				"gemini-embedding-001",
 				2048,
 			)
 		})
@@ -109,8 +126,8 @@ describe("GeminiEmbedder", () => {
 			})
 
 			it("should use provided model parameter when specified", async () => {
-				// Arrange
-				embedder = new GeminiEmbedder("test-api-key", "text-embedding-004")
+				// Arrange - even with deprecated model in constructor, the runtime parameter takes precedence
+				embedder = new GeminiEmbedder("test-api-key", "gemini-embedding-001")
 				const texts = ["test text 1", "test text 2"]
 				const mockResponse = {
 					embeddings: [
@@ -120,7 +137,7 @@ describe("GeminiEmbedder", () => {
 				}
 				mockCreateEmbeddings.mockResolvedValue(mockResponse)
 
-				// Act
+				// Act - specify a different model at runtime
 				const result = await embedder.createEmbeddings(texts, "gemini-embedding-001")
 
 				// Assert

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

@@ -10,15 +10,33 @@ import { TelemetryService } from "@roo-code/telemetry"
  * with configuration for Google's Gemini embedding API.
  *
  * Supported models:
- * - text-embedding-004 (dimension: 768)
- * - gemini-embedding-001 (dimension: 2048)
+ * - gemini-embedding-001 (dimension: 3072)
+ *
+ * Note: text-embedding-004 has been deprecated and is automatically
+ * migrated to gemini-embedding-001 for backward compatibility.
  */
 export class GeminiEmbedder implements IEmbedder {
 	private readonly openAICompatibleEmbedder: OpenAICompatibleEmbedder
 	private static readonly GEMINI_BASE_URL = "https://generativelanguage.googleapis.com/v1beta/openai/"
 	private static readonly DEFAULT_MODEL = "gemini-embedding-001"
+	/**
+	 * Deprecated models that are automatically migrated to their replacements.
+	 * Users with these models configured will be silently migrated without interruption.
+	 */
+	private static readonly DEPRECATED_MODEL_MIGRATIONS: Record<string, string> = {
+		"text-embedding-004": "gemini-embedding-001",
+	}
 	private readonly modelId: string
 
+	/**
+	 * Migrates deprecated model IDs to their replacements.
+	 * @param modelId The model ID to potentially migrate
+	 * @returns The migrated model ID, or the original if no migration is needed
+	 */
+	private static migrateModelId(modelId: string): string {
+		return GeminiEmbedder.DEPRECATED_MODEL_MIGRATIONS[modelId] ?? modelId
+	}
+
 	/**
 	 * Creates a new Gemini embedder
 	 * @param apiKey The Gemini API key for authentication
@@ -29,8 +47,11 @@ export class GeminiEmbedder implements IEmbedder {
 			throw new Error(t("embeddings:validation.apiKeyRequired"))
 		}
 
-		// Use provided model or default
-		this.modelId = modelId || GeminiEmbedder.DEFAULT_MODEL
+		// Migrate deprecated models to their replacements silently
+		const migratedModelId = modelId ? GeminiEmbedder.migrateModelId(modelId) : undefined
+
+		// Use provided model (after migration) or default
+		this.modelId = migratedModelId || GeminiEmbedder.DEFAULT_MODEL
 
 		// Create an OpenAI Compatible embedder with Gemini's configuration
 		this.openAICompatibleEmbedder = new OpenAICompatibleEmbedder(

src/shared/__tests__/embeddingModels.spec.ts

@@ -0,0 +1,95 @@
+import { describe, it, expect } from "vitest"
+import {
+	getModelDimension,
+	getModelScoreThreshold,
+	getDefaultModelId,
+	EMBEDDING_MODEL_PROFILES,
+} from "../embeddingModels"
+
+describe("embeddingModels", () => {
+	describe("EMBEDDING_MODEL_PROFILES", () => {
+		it("should have gemini provider with gemini-embedding-001 model", () => {
+			const geminiProfiles = EMBEDDING_MODEL_PROFILES.gemini
+			expect(geminiProfiles).toBeDefined()
+			expect(geminiProfiles!["gemini-embedding-001"]).toBeDefined()
+			expect(geminiProfiles!["gemini-embedding-001"].dimension).toBe(3072)
+		})
+
+		it("should have deprecated text-embedding-004 in gemini profiles for backward compatibility", () => {
+			// This is critical for backward compatibility:
+			// Users with text-embedding-004 configured need dimension lookup to work
+			// even though the model is migrated to gemini-embedding-001 in GeminiEmbedder
+			const geminiProfiles = EMBEDDING_MODEL_PROFILES.gemini
+			expect(geminiProfiles).toBeDefined()
+			expect(geminiProfiles!["text-embedding-004"]).toBeDefined()
+			expect(geminiProfiles!["text-embedding-004"].dimension).toBe(3072)
+		})
+	})
+
+	describe("getModelDimension", () => {
+		it("should return dimension for gemini-embedding-001", () => {
+			const dimension = getModelDimension("gemini", "gemini-embedding-001")
+			expect(dimension).toBe(3072)
+		})
+
+		it("should return dimension for deprecated text-embedding-004", () => {
+			// This ensures createVectorStore() works for users with text-embedding-004 configured
+			// The dimension should be 3072 (matching gemini-embedding-001) because:
+			// 1. GeminiEmbedder migrates text-embedding-004 to gemini-embedding-001
+			// 2. gemini-embedding-001 produces 3072-dimensional embeddings
+			// 3. Vector store dimension must match the actual embedding dimension
+			const dimension = getModelDimension("gemini", "text-embedding-004")
+			expect(dimension).toBe(3072)
+		})
+
+		it("should return undefined for unknown model", () => {
+			const dimension = getModelDimension("gemini", "unknown-model")
+			expect(dimension).toBeUndefined()
+		})
+
+		it("should return undefined for unknown provider", () => {
+			const dimension = getModelDimension("unknown-provider" as any, "some-model")
+			expect(dimension).toBeUndefined()
+		})
+
+		it("should return correct dimensions for openai models", () => {
+			expect(getModelDimension("openai", "text-embedding-3-small")).toBe(1536)
+			expect(getModelDimension("openai", "text-embedding-3-large")).toBe(3072)
+			expect(getModelDimension("openai", "text-embedding-ada-002")).toBe(1536)
+		})
+	})
+
+	describe("getModelScoreThreshold", () => {
+		it("should return score threshold for gemini-embedding-001", () => {
+			const threshold = getModelScoreThreshold("gemini", "gemini-embedding-001")
+			expect(threshold).toBe(0.4)
+		})
+
+		it("should return score threshold for deprecated text-embedding-004", () => {
+			const threshold = getModelScoreThreshold("gemini", "text-embedding-004")
+			expect(threshold).toBe(0.4)
+		})
+
+		it("should return undefined for unknown model", () => {
+			const threshold = getModelScoreThreshold("gemini", "unknown-model")
+			expect(threshold).toBeUndefined()
+		})
+	})
+
+	describe("getDefaultModelId", () => {
+		it("should return gemini-embedding-001 for gemini provider", () => {
+			const defaultModel = getDefaultModelId("gemini")
+			expect(defaultModel).toBe("gemini-embedding-001")
+		})
+
+		it("should return text-embedding-3-small for openai provider", () => {
+			const defaultModel = getDefaultModelId("openai")
+			expect(defaultModel).toBe("text-embedding-3-small")
+		})
+
+		it("should return codestral-embed-2505 for mistral provider", () => {
+			const defaultModel = getDefaultModelId("mistral")
+			expect(defaultModel).toBe("codestral-embed-2505")
+		})
+	})
+})

src/shared/embeddingModels.ts

@@ -34,8 +34,10 @@ export const EMBEDDING_MODEL_PROFILES: EmbeddingModelProfiles = {
 		},
 	},
 	gemini: {
-		"text-embedding-004": { dimension: 768 },
 		"gemini-embedding-001": { dimension: 3072, scoreThreshold: 0.4 },
+		// Deprecated: text-embedding-004 is migrated to gemini-embedding-001 in GeminiEmbedder
+		// Kept here for backward-compatible dimension lookup in createVectorStore()
+		"text-embedding-004": { dimension: 3072, scoreThreshold: 0.4 },
 	},
 	mistral: {
 		"codestral-embed-2505": { dimension: 1536, scoreThreshold: 0.4 },