feat: add support for Google Gemini embedding model gemini-embedding-exp-03-07

- Add gemini-embedding-exp-03-07 model variants with flexible dimensions (3072, 1536, 768)
- Update GeminiEmbedder to support outputDimension parameter
- Update OpenAICompatibleEmbedder to handle outputDimension in API calls
- Set gemini-embedding-exp-03-07-3072 as new default model for Gemini provider
- Preserve backward compatibility with existing text-embedding-004 model
- Update tests to cover new model and outputDimension functionality

Fixes #5621

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

@@ -36,8 +36,9 @@ describe("GeminiEmbedder", () => {
 			expect(MockedOpenAICompatibleEmbedder).toHaveBeenCalledWith(
 				"https://generativelanguage.googleapis.com/v1beta/openai/",
 				apiKey,
-				"gemini-embedding-001",
+				"gemini-embedding-exp-03-07-3072",
 				2048,
+				undefined,
 			)
 		})
 
@@ -55,6 +56,26 @@ describe("GeminiEmbedder", () => {
 				apiKey,
 				"text-embedding-004",
 				2048,
+				undefined,
+			)
+		})
+
+		it("should create an instance with specified model and outputDimension", () => {
+			// Arrange
+			const apiKey = "test-gemini-api-key"
+			const modelId = "gemini-embedding-exp-03-07-1536"
+			const outputDimension = 1536
+
+			// Act
+			embedder = new GeminiEmbedder(apiKey, modelId, outputDimension)
+
+			// Assert
+			expect(MockedOpenAICompatibleEmbedder).toHaveBeenCalledWith(
+				"https://generativelanguage.googleapis.com/v1beta/openai/",
+				apiKey,
+				"gemini-embedding-exp-03-07-1536",
+				2048,
+				1536,
 			)
 		})
 
@@ -104,7 +125,7 @@ describe("GeminiEmbedder", () => {
 				const result = await embedder.createEmbeddings(texts)
 
 				// Assert
-				expect(mockCreateEmbeddings).toHaveBeenCalledWith(texts, "gemini-embedding-001")
+				expect(mockCreateEmbeddings).toHaveBeenCalledWith(texts, "gemini-embedding-exp-03-07-3072")
 				expect(result).toEqual(mockResponse)
 			})
 

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

@@ -11,20 +11,24 @@ import { TelemetryService } from "@roo-code/telemetry"
  *
  * Supported models:
  * - text-embedding-004 (dimension: 768)
- * - gemini-embedding-001 (dimension: 2048)
+ * - gemini-embedding-001 (dimension: 3072)
+ * - gemini-embedding-exp-03-07-3072 (dimension: 3072)
+ * - gemini-embedding-exp-03-07-1536 (dimension: 1536)
+ * - gemini-embedding-exp-03-07-768 (dimension: 768)
  */
 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"
+	private static readonly DEFAULT_MODEL = "gemini-embedding-exp-03-07-3072"
 	private readonly modelId: string
 
 	/**
 	 * Creates a new Gemini embedder
 	 * @param apiKey The Gemini API key for authentication
-	 * @param modelId The model ID to use (defaults to gemini-embedding-001)
+	 * @param modelId The model ID to use (defaults to gemini-embedding-exp-03-07-3072)
+	 * @param outputDimension Optional output dimension for flexible models
 	 */
-	constructor(apiKey: string, modelId?: string) {
+	constructor(apiKey: string, modelId?: string, outputDimension?: number) {
 		if (!apiKey) {
 			throw new Error(t("embeddings:validation.apiKeyRequired"))
 		}
@@ -38,6 +42,7 @@ export class GeminiEmbedder implements IEmbedder {
 			apiKey,
 			this.modelId,
 			GEMINI_MAX_ITEM_TOKENS,
+			outputDimension,
 		)
 	}
 

src/services/code-index/embedders/openai-compatible.ts

@@ -38,6 +38,7 @@ export class OpenAICompatibleEmbedder implements IEmbedder {
 	private readonly apiKey: string
 	private readonly isFullUrl: boolean
 	private readonly maxItemTokens: number
+	private readonly outputDimension?: number
 
 	// Global rate limiting state shared across all instances
 	private static globalRateLimitState = {
@@ -55,8 +56,9 @@ export class OpenAICompatibleEmbedder implements IEmbedder {
 	 * @param apiKey The API key for authentication
 	 * @param modelId Optional model identifier (defaults to "text-embedding-3-small")
 	 * @param maxItemTokens Optional maximum tokens per item (defaults to MAX_ITEM_TOKENS)
+	 * @param outputDimension Optional output dimension for flexible models
 	 */
-	constructor(baseUrl: string, apiKey: string, modelId?: string, maxItemTokens?: number) {
+	constructor(baseUrl: string, apiKey: string, modelId?: string, maxItemTokens?: number, outputDimension?: number) {
 		if (!baseUrl) {
 			throw new Error(t("embeddings:validation.baseUrlRequired"))
 		}
@@ -74,6 +76,7 @@ export class OpenAICompatibleEmbedder implements IEmbedder {
 		// Cache the URL type check for performance
 		this.isFullUrl = this.isFullEndpointUrl(baseUrl)
 		this.maxItemTokens = maxItemTokens || MAX_ITEM_TOKENS
+		this.outputDimension = outputDimension
 	}
 
 	/**
@@ -208,6 +211,7 @@ export class OpenAICompatibleEmbedder implements IEmbedder {
 				input: batchTexts,
 				model: model,
 				encoding_format: "base64",
+				...(this.outputDimension && { dimensions: this.outputDimension }),
 			}),
 		})
 
@@ -270,6 +274,7 @@ export class OpenAICompatibleEmbedder implements IEmbedder {
 						// when processing numeric arrays, which breaks compatibility with models using larger dimensions.
 						// By requesting base64 encoding, we bypass the package's parser and handle decoding ourselves.
 						encoding_format: "base64",
+						...(this.outputDimension && { dimensions: this.outputDimension }),
 					})) as OpenAIEmbeddingResponse
 				}
 
@@ -369,6 +374,7 @@ export class OpenAICompatibleEmbedder implements IEmbedder {
 						input: testTexts,
 						model: modelToUse,
 						encoding_format: "base64",
+						...(this.outputDimension && { dimensions: this.outputDimension }),
 					})) as OpenAIEmbeddingResponse
 				}
 

src/shared/embeddingModels.ts

@@ -49,6 +49,9 @@ export const EMBEDDING_MODEL_PROFILES: EmbeddingModelProfiles = {
 	gemini: {
 		"text-embedding-004": { dimension: 768 },
 		"gemini-embedding-001": { dimension: 3072, scoreThreshold: 0.4 },
+		"gemini-embedding-exp-03-07-3072": { dimension: 3072, scoreThreshold: 0.4 },
+		"gemini-embedding-exp-03-07-1536": { dimension: 1536, scoreThreshold: 0.4 },
+		"gemini-embedding-exp-03-07-768": { dimension: 768, scoreThreshold: 0.4 },
 	},
 	mistral: {
 		"codestral-embed-2505": { dimension: 1536, scoreThreshold: 0.4 },
@@ -155,7 +158,7 @@ export function getDefaultModelId(provider: EmbedderProvider): string {
 		}
 
 		case "gemini":
-			return "gemini-embedding-001"
+			return "gemini-embedding-exp-03-07-3072"
 
 		case "mistral":
 			return "codestral-embed-2505"