fix: exclude encoding_format parameter for Google Gemini embeddings API

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

@@ -1051,4 +1051,139 @@ describe("OpenAICompatibleEmbedder", () => {
 			expect(result.error).toBe("embeddings:validation.configurationError")
 		})
 	})
+
+	describe("Gemini compatibility", () => {
+		let geminiEmbedder: OpenAICompatibleEmbedder
+		const geminiBaseUrl = "https://generativelanguage.googleapis.com/v1beta/openai/"
+		const geminiApiKey = "test-gemini-api-key"
+		const geminiModelId = "gemini-embedding-001"
+
+		beforeEach(() => {
+			vitest.clearAllMocks()
+			geminiEmbedder = new OpenAICompatibleEmbedder(geminiBaseUrl, geminiApiKey, geminiModelId)
+		})
+
+		it("should NOT include encoding_format for Gemini endpoints", async () => {
+			const testTexts = ["Hello world"]
+			const mockResponse = {
+				data: [{ embedding: [0.1, 0.2, 0.3] }],
+				usage: { prompt_tokens: 10, total_tokens: 15 },
+			}
+			mockEmbeddingsCreate.mockResolvedValue(mockResponse)
+
+			await geminiEmbedder.createEmbeddings(testTexts)
+
+			// Verify that encoding_format is NOT included for Gemini
+			expect(mockEmbeddingsCreate).toHaveBeenCalledWith({
+				input: testTexts,
+				model: geminiModelId,
+				// encoding_format should NOT be present
+			})
+
+			// Verify the call doesn't have encoding_format property
+			const callArgs = mockEmbeddingsCreate.mock.calls[0][0]
+			expect(callArgs).not.toHaveProperty("encoding_format")
+		})
+
+		it("should still include encoding_format for non-Gemini OpenAI-compatible endpoints", async () => {
+			// Create a non-Gemini embedder
+			const regularEmbedder = new OpenAICompatibleEmbedder(testBaseUrl, testApiKey, testModelId)
+
+			const testTexts = ["Hello world"]
+			const mockResponse = {
+				data: [{ embedding: [0.1, 0.2, 0.3] }],
+				usage: { prompt_tokens: 10, total_tokens: 15 },
+			}
+			mockEmbeddingsCreate.mockResolvedValue(mockResponse)
+
+			await regularEmbedder.createEmbeddings(testTexts)
+
+			// Verify that encoding_format IS included for non-Gemini endpoints
+			expect(mockEmbeddingsCreate).toHaveBeenCalledWith({
+				input: testTexts,
+				model: testModelId,
+				encoding_format: "base64",
+			})
+		})
+
+		it("should correctly identify Gemini URLs", () => {
+			const geminiUrls = [
+				"https://generativelanguage.googleapis.com/v1beta/openai/",
+				"https://generativelanguage.googleapis.com/v1/openai/",
+				"https://generativelanguage.googleapis.com/v2/embeddings",
+			]
+
+			geminiUrls.forEach((url) => {
+				const embedder = new OpenAICompatibleEmbedder(url, geminiApiKey, geminiModelId)
+				const isGemini = (embedder as any).isGeminiUrl(url)
+				expect(isGemini).toBe(true)
+			})
+		})
+
+		it("should not identify non-Gemini URLs as Gemini", () => {
+			const nonGeminiUrls = [
+				"https://api.openai.com/v1",
+				"https://api.example.com/embeddings",
+				"https://myinstance.openai.azure.com/openai/deployments/my-deployment/embeddings",
+				"http://localhost:8080",
+			]
+
+			nonGeminiUrls.forEach((url) => {
+				const embedder = new OpenAICompatibleEmbedder(url, testApiKey, testModelId)
+				const isGemini = (embedder as any).isGeminiUrl(url)
+				expect(isGemini).toBe(false)
+			})
+		})
+
+		it("should validate Gemini configuration without encoding_format", async () => {
+			const mockResponse = {
+				data: [{ embedding: [0.1, 0.2, 0.3] }],
+				usage: { prompt_tokens: 2, total_tokens: 2 },
+			}
+			mockEmbeddingsCreate.mockResolvedValue(mockResponse)
+
+			const result = await geminiEmbedder.validateConfiguration()
+
+			expect(result.valid).toBe(true)
+			expect(result.error).toBeUndefined()
+
+			// Verify validation call doesn't include encoding_format
+			const callArgs = mockEmbeddingsCreate.mock.calls[0][0]
+			expect(callArgs).not.toHaveProperty("encoding_format")
+		})
+
+		it("should handle direct HTTP requests for Gemini full URLs without encoding_format", async () => {
+			const geminiFullUrl = "https://generativelanguage.googleapis.com/v1beta/openai/embeddings"
+			const fullUrlEmbedder = new OpenAICompatibleEmbedder(geminiFullUrl, geminiApiKey, geminiModelId)
+
+			const mockFetch = vitest.fn().mockResolvedValue({
+				ok: true,
+				json: async () => ({
+					data: [{ embedding: [0.1, 0.2, 0.3] }],
+					usage: { prompt_tokens: 10, total_tokens: 15 },
+				}),
+			})
+			global.fetch = mockFetch
+
+			await fullUrlEmbedder.createEmbeddings(["test"])
+
+			// Check that the request body doesn't include encoding_format
+			expect(mockFetch).toHaveBeenCalledWith(
+				geminiFullUrl,
+				expect.objectContaining({
+					method: "POST",
+					body: JSON.stringify({
+						input: ["test"],
+						model: geminiModelId,
+						// encoding_format should NOT be present
+					}),
+				}),
+			)
+
+			// Verify the actual body content
+			const callArgs = mockFetch.mock.calls[0][1]
+			const bodyContent = JSON.parse(callArgs.body)
+			expect(bodyContent).not.toHaveProperty("encoding_format")
+		})
+	})
 })

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

@@ -38,6 +38,7 @@
 	private readonly apiKey: string
 	private readonly isFullUrl: boolean
 	private readonly maxItemTokens: number
+	private readonly isGeminiEndpoint: boolean
 
 	// Global rate limiting state shared across all instances
 	private static globalRateLimitState = {
@@ -73,6 +74,7 @@
 		this.defaultModelId = modelId || getDefaultModelId("openai-compatible")
 		// Cache the URL type check for performance
 		this.isFullUrl = this.isFullEndpointUrl(baseUrl)
+		this.isGeminiEndpoint = this.isGeminiUrl(baseUrl)
 		this.maxItemTokens = maxItemTokens || MAX_ITEM_TOKENS
 	}
 
@@ -182,6 +184,15 @@
 		return patterns.some((pattern) => pattern.test(url))
 	}
 
+	/**
+	 * Determines if the provided URL is a Google Gemini endpoint
+	 * @param url The URL to check
+	 * @returns true if it's a Gemini endpoint
+	 */
+	private isGeminiUrl(url: string): boolean {
+		return url.includes("generativelanguage.googleapis.com")
+	}
+
 	/**
 	 * Makes a direct HTTP request to the embeddings endpoint
 	 * Used when the user provides a full endpoint URL (e.g., Azure OpenAI with query parameters)
@@ -204,11 +215,19 @@
 				"api-key": this.apiKey,
 				Authorization: `Bearer ${this.apiKey}`,
 			},
-			body: JSON.stringify({
-				input: batchTexts,
-				model: model,
-				encoding_format: "base64",
-			}),
+			body: JSON.stringify(
+				this.isGeminiEndpoint
+					? {
+							input: batchTexts,
+							model: model,
+							// Gemini doesn't support encoding_format parameter
+						}
+					: {
+							input: batchTexts,
+							model: model,
+							encoding_format: "base64",
+						},
+			),
 		})
 
 		if (!response || !response.ok) {
@@ -263,14 +282,23 @@
 					response = await this.makeDirectEmbeddingRequest(this.baseUrl, batchTexts, model)
 				} else {
 					// Use OpenAI SDK for base URLs
-					response = (await this.embeddingsClient.embeddings.create({
+					const embeddingParams: any = {
 						input: batchTexts,
 						model: model,
-						// OpenAI package (as of v4.78.1) has a parsing issue that truncates embedding dimensions to 256
-						// 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",
-					})) as OpenAIEmbeddingResponse
+					}
+
+					// Only add encoding_format for non-Gemini endpoints
+					// OpenAI package (as of v4.78.1) has a parsing issue that truncates embedding dimensions to 256
+					// 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.
+					// However, Gemini doesn't support this parameter, so we exclude it for Gemini endpoints.
+					if (!this.isGeminiEndpoint) {
+						embeddingParams.encoding_format = "base64"
+					}
+
+					response = (await this.embeddingsClient.embeddings.create(
+						embeddingParams,
+					)) as OpenAIEmbeddingResponse
 				}
 
 				// Convert base64 embeddings to float32 arrays
@@ -365,11 +393,19 @@
 					response = await this.makeDirectEmbeddingRequest(this.baseUrl, testTexts, modelToUse)
 				} else {
 					// Test using OpenAI SDK for base URLs
-					response = (await this.embeddingsClient.embeddings.create({
+					const embeddingParams: any = {
 						input: testTexts,
 						model: modelToUse,
-						encoding_format: "base64",
-					})) as OpenAIEmbeddingResponse
+					}
+
+					// Only add encoding_format for non-Gemini endpoints
+					if (!this.isGeminiEndpoint) {
+						embeddingParams.encoding_format = "base64"
+					}
+
+					response = (await this.embeddingsClient.embeddings.create(
+						embeddingParams,
+					)) as OpenAIEmbeddingResponse
 				}
 
 				// Check if we got a valid response