fix: separate document and query prefixes for embedding models

packages/types/src/embedding.ts

@@ -8,10 +8,13 @@ export type EmbedderProvider =
 	| "bedrock"
 	| "openrouter" // Add other providers as needed.
 
+export type EmbeddingPurpose = "index" | "query"
+
 export interface EmbeddingModelProfile {
 	dimension: number
 	scoreThreshold?: number // Model-specific minimum score threshold for semantic search.
-	queryPrefix?: string // Optional prefix required by the model for queries.
+	queryPrefix?: string // Optional prefix required by the model for search queries.
+	documentPrefix?: string // Optional prefix required by the model for indexing/document embedding.
 	// Add other model-specific properties if needed, e.g., context window size.
 }
 

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

@@ -121,7 +121,7 @@ describe("GeminiEmbedder", () => {
 				const result = await embedder.createEmbeddings(texts)
 
 				// Assert
-				expect(mockCreateEmbeddings).toHaveBeenCalledWith(texts, "gemini-embedding-001")
+				expect(mockCreateEmbeddings).toHaveBeenCalledWith(texts, "gemini-embedding-001", undefined)
 				expect(result).toEqual(mockResponse)
 			})
 
@@ -141,7 +141,7 @@ describe("GeminiEmbedder", () => {
 				const result = await embedder.createEmbeddings(texts, "gemini-embedding-001")
 
 				// Assert
-				expect(mockCreateEmbeddings).toHaveBeenCalledWith(texts, "gemini-embedding-001")
+				expect(mockCreateEmbeddings).toHaveBeenCalledWith(texts, "gemini-embedding-001", undefined)
 				expect(result).toEqual(mockResponse)
 			})
 

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

@@ -104,7 +104,7 @@ describe("MistralEmbedder", () => {
 				const result = await embedder.createEmbeddings(texts)
 
 				// Assert
-				expect(mockCreateEmbeddings).toHaveBeenCalledWith(texts, "codestral-embed-2505")
+				expect(mockCreateEmbeddings).toHaveBeenCalledWith(texts, "codestral-embed-2505", undefined)
 				expect(result).toEqual(mockResponse)
 			})
 
@@ -124,7 +124,7 @@ describe("MistralEmbedder", () => {
 				const result = await embedder.createEmbeddings(texts, "codestral-embed-2505")
 
 				// Assert
-				expect(mockCreateEmbeddings).toHaveBeenCalledWith(texts, "codestral-embed-2505")
+				expect(mockCreateEmbeddings).toHaveBeenCalledWith(texts, "codestral-embed-2505", undefined)
 				expect(result).toEqual(mockResponse)
 			})
 

src/services/code-index/embedders/__tests__/vercel-ai-gateway.spec.ts

@@ -95,6 +95,7 @@ describe("VercelAiGatewayEmbedder", () => {
 			expect(mockOpenAICompatibleEmbedder.createEmbeddings).toHaveBeenCalledWith(
 				texts,
 				"openai/text-embedding-3-large",
+				undefined,
 			)
 			expect(result).toBe(expectedResponse)
 		})
@@ -110,7 +111,7 @@ describe("VercelAiGatewayEmbedder", () => {
 			const result = await embedder.createEmbeddings(texts, customModel)
 
 			// Assert
-			expect(mockOpenAICompatibleEmbedder.createEmbeddings).toHaveBeenCalledWith(texts, customModel)
+			expect(mockOpenAICompatibleEmbedder.createEmbeddings).toHaveBeenCalledWith(texts, customModel, undefined)
 			expect(result).toBe(expectedResponse)
 		})
 
@@ -125,6 +126,7 @@ describe("VercelAiGatewayEmbedder", () => {
 			expect(mockOpenAICompatibleEmbedder.createEmbeddings).toHaveBeenCalledWith(
 				texts,
 				"openai/text-embedding-3-large",
+				undefined,
 			)
 		})
 	})

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

@@ -1,3 +1,4 @@
+import type { EmbeddingPurpose } from "@roo-code/types"
 import { BedrockRuntimeClient, InvokeModelCommand, InvokeModelCommandInput } from "@aws-sdk/client-bedrock-runtime"
 import { fromIni, fromNodeProviderChain } from "@aws-sdk/credential-providers"
 import { IEmbedder, EmbeddingResponse, EmbedderInfo } from "../interfaces"
@@ -55,7 +56,7 @@ export class BedrockEmbedder implements IEmbedder {
 	 * @param model Optional model identifier
 	 * @returns Promise resolving to embedding response
 	 */
-	async createEmbeddings(texts: string[], model?: string): Promise<EmbeddingResponse> {
+	async createEmbeddings(texts: string[], model?: string, _purpose?: EmbeddingPurpose): Promise<EmbeddingResponse> {
 		const modelToUse = model || this.defaultModelId
 
 		const allEmbeddings: number[][] = []

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

@@ -1,3 +1,4 @@
+import type { EmbeddingPurpose } from "@roo-code/types"
 import { OpenAICompatibleEmbedder } from "./openai-compatible"
 import { IEmbedder, EmbeddingResponse, EmbedderInfo } from "../interfaces/embedder"
 import { GEMINI_MAX_ITEM_TOKENS } from "../constants"
@@ -68,11 +69,11 @@ export class GeminiEmbedder implements IEmbedder {
 	 * @param model Optional model identifier (uses constructor model if not provided)
 	 * @returns Promise resolving to embedding response
 	 */
-	async createEmbeddings(texts: string[], model?: string): Promise<EmbeddingResponse> {
+	async createEmbeddings(texts: string[], model?: string, purpose?: EmbeddingPurpose): Promise<EmbeddingResponse> {
 		try {
 			// Use the provided model or fall back to the instance's model
 			const modelToUse = model || this.modelId
-			return await this.openAICompatibleEmbedder.createEmbeddings(texts, modelToUse)
+			return await this.openAICompatibleEmbedder.createEmbeddings(texts, modelToUse, purpose)
 		} catch (error) {
 			TelemetryService.instance.captureEvent(TelemetryEventName.CODE_INDEX_ERROR, {
 				error: error instanceof Error ? error.message : String(error),

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

@@ -1,3 +1,4 @@
+import type { EmbeddingPurpose } from "@roo-code/types"
 import { OpenAICompatibleEmbedder } from "./openai-compatible"
 import { IEmbedder, EmbeddingResponse, EmbedderInfo } from "../interfaces/embedder"
 import { MAX_ITEM_TOKENS } from "../constants"
@@ -46,11 +47,11 @@ export class MistralEmbedder implements IEmbedder {
 	 * @param model Optional model identifier (uses constructor model if not provided)
 	 * @returns Promise resolving to embedding response
 	 */
-	async createEmbeddings(texts: string[], model?: string): Promise<EmbeddingResponse> {
+	async createEmbeddings(texts: string[], model?: string, purpose?: EmbeddingPurpose): Promise<EmbeddingResponse> {
 		try {
 			// Use the provided model or fall back to the instance's model
 			const modelToUse = model || this.modelId
-			return await this.openAICompatibleEmbedder.createEmbeddings(texts, modelToUse)
+			return await this.openAICompatibleEmbedder.createEmbeddings(texts, modelToUse, purpose)
 		} catch (error) {
 			TelemetryService.instance.captureEvent(TelemetryEventName.CODE_INDEX_ERROR, {
 				error: error instanceof Error ? error.message : String(error),

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

@@ -1,6 +1,7 @@
+import type { EmbeddingPurpose } from "@roo-code/types"
 import { ApiHandlerOptions } from "../../../shared/api"
 import { EmbedderInfo, EmbeddingResponse, IEmbedder } from "../interfaces"
-import { getModelQueryPrefix } from "../../../shared/embeddingModels"
+import { getModelPrefixForPurpose } from "../../../shared/embeddingModels"
 import { MAX_ITEM_TOKENS } from "../constants"
 import { t } from "../../../i18n"
 import { withValidationErrorHandling, sanitizeErrorMessage } from "../shared/validation-helpers"
@@ -35,12 +36,12 @@ export class CodeIndexOllamaEmbedder implements IEmbedder {
 	 * @param model - Optional model ID to override the default.
 	 * @returns A promise that resolves to an EmbeddingResponse containing the embeddings and usage data.
 	 */
-	async createEmbeddings(texts: string[], model?: string): Promise<EmbeddingResponse> {
+	async createEmbeddings(texts: string[], model?: string, purpose?: EmbeddingPurpose): Promise<EmbeddingResponse> {
 		const modelToUse = model || this.defaultModelId
 		const url = `${this.baseUrl}/api/embed` // Endpoint as specified
 
-		// Apply model-specific query prefix if required
-		const queryPrefix = getModelQueryPrefix("ollama", modelToUse)
+		// Apply model-specific prefix based on purpose (query vs index)
+		const queryPrefix = getModelPrefixForPurpose("ollama", modelToUse, purpose)
 		const processedTexts = queryPrefix
 			? texts.map((text, index) => {
 					// Prevent double-prefixing

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

@@ -1,3 +1,4 @@
+import type { EmbeddingPurpose } from "@roo-code/types"
 import { OpenAI } from "openai"
 import { IEmbedder, EmbeddingResponse, EmbedderInfo } from "../interfaces/embedder"
 import {
@@ -6,7 +7,7 @@ import {
 	MAX_BATCH_RETRIES as MAX_RETRIES,
 	INITIAL_RETRY_DELAY_MS as INITIAL_DELAY_MS,
 } from "../constants"
-import { getDefaultModelId, getModelQueryPrefix } from "../../../shared/embeddingModels"
+import { getDefaultModelId, getModelPrefixForPurpose } from "../../../shared/embeddingModels"
 import { t } from "../../../i18n"
 import { withValidationErrorHandling, HttpError, formatEmbeddingError } from "../shared/validation-helpers"
 import { TelemetryEventName } from "@roo-code/types"
@@ -91,11 +92,11 @@ export class OpenAICompatibleEmbedder implements IEmbedder {
 	 * @param model Optional model identifier
 	 * @returns Promise resolving to embedding response
 	 */
-	async createEmbeddings(texts: string[], model?: string): Promise<EmbeddingResponse> {
+	async createEmbeddings(texts: string[], model?: string, purpose?: EmbeddingPurpose): Promise<EmbeddingResponse> {
 		const modelToUse = model || this.defaultModelId
 
-		// Apply model-specific query prefix if required
-		const queryPrefix = getModelQueryPrefix("openai-compatible", modelToUse)
+		// Apply model-specific prefix based on purpose (query vs index)
+		const queryPrefix = getModelPrefixForPurpose("openai-compatible", modelToUse, purpose)
 		const processedTexts = queryPrefix
 			? texts.map((text, index) => {
 					// Prevent double-prefixing

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

@@ -1,3 +1,4 @@
+import type { EmbeddingPurpose } from "@roo-code/types"
 import { OpenAI } from "openai"
 import { OpenAiNativeHandler } from "../../../api/providers/openai-native"
 import { ApiHandlerOptions } from "../../../shared/api"
@@ -8,7 +9,7 @@ import {
 	MAX_BATCH_RETRIES as MAX_RETRIES,
 	INITIAL_RETRY_DELAY_MS as INITIAL_DELAY_MS,
 } from "../constants"
-import { getModelQueryPrefix } from "../../../shared/embeddingModels"
+import { getModelPrefixForPurpose } from "../../../shared/embeddingModels"
 import { t } from "../../../i18n"
 import { withValidationErrorHandling, formatEmbeddingError, HttpError } from "../shared/validation-helpers"
 import { TelemetryEventName } from "@roo-code/types"
@@ -47,11 +48,11 @@ export class OpenAiEmbedder extends OpenAiNativeHandler implements IEmbedder {
 	 * @param model Optional model identifier
 	 * @returns Promise resolving to embedding response
 	 */
-	async createEmbeddings(texts: string[], model?: string): Promise<EmbeddingResponse> {
+	async createEmbeddings(texts: string[], model?: string, purpose?: EmbeddingPurpose): Promise<EmbeddingResponse> {
 		const modelToUse = model || this.defaultModelId
 
-		// Apply model-specific query prefix if required
-		const queryPrefix = getModelQueryPrefix("openai", modelToUse)
+		// Apply model-specific prefix based on purpose (query vs index)
+		const queryPrefix = getModelPrefixForPurpose("openai", modelToUse, purpose)
 		const processedTexts = queryPrefix
 			? texts.map((text, index) => {
 					// Prevent double-prefixing

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

@@ -1,3 +1,4 @@
+import type { EmbeddingPurpose } from "@roo-code/types"
 import { OpenAI } from "openai"
 import { IEmbedder, EmbeddingResponse, EmbedderInfo } from "../interfaces/embedder"
 import {
@@ -6,7 +7,7 @@ import {
 	MAX_BATCH_RETRIES as MAX_RETRIES,
 	INITIAL_RETRY_DELAY_MS as INITIAL_DELAY_MS,
 } from "../constants"
-import { getDefaultModelId, getModelQueryPrefix } from "../../../shared/embeddingModels"
+import { getDefaultModelId, getModelPrefixForPurpose } from "../../../shared/embeddingModels"
 import { t } from "../../../i18n"
 import { withValidationErrorHandling, HttpError, formatEmbeddingError } from "../shared/validation-helpers"
 import { TelemetryEventName } from "@roo-code/types"
@@ -95,11 +96,11 @@ export class OpenRouterEmbedder implements IEmbedder {
 	 * @param model Optional model identifier
 	 * @returns Promise resolving to embedding response
 	 */
-	async createEmbeddings(texts: string[], model?: string): Promise<EmbeddingResponse> {
+	async createEmbeddings(texts: string[], model?: string, purpose?: EmbeddingPurpose): Promise<EmbeddingResponse> {
 		const modelToUse = model || this.defaultModelId
 
-		// Apply model-specific query prefix if required
-		const queryPrefix = getModelQueryPrefix("openrouter", modelToUse)
+		// Apply model-specific prefix based on purpose (query vs index)
+		const queryPrefix = getModelPrefixForPurpose("openrouter", modelToUse, purpose)
 		const processedTexts = queryPrefix
 			? texts.map((text, index) => {
 					// Prevent double-prefixing

src/services/code-index/embedders/vercel-ai-gateway.ts

@@ -1,3 +1,4 @@
+import type { EmbeddingPurpose } from "@roo-code/types"
 import { OpenAICompatibleEmbedder } from "./openai-compatible"
 import { IEmbedder, EmbeddingResponse, EmbedderInfo } from "../interfaces/embedder"
 import { MAX_ITEM_TOKENS } from "../constants"
@@ -55,11 +56,11 @@ export class VercelAiGatewayEmbedder implements IEmbedder {
 	 * @param model Optional model identifier (uses constructor model if not provided)
 	 * @returns Promise resolving to embedding response
 	 */
-	async createEmbeddings(texts: string[], model?: string): Promise<EmbeddingResponse> {
+	async createEmbeddings(texts: string[], model?: string, purpose?: EmbeddingPurpose): Promise<EmbeddingResponse> {
 		try {
 			// Use the provided model or fall back to the instance's model
 			const modelToUse = model || this.modelId
-			return await this.openAICompatibleEmbedder.createEmbeddings(texts, modelToUse)
+			return await this.openAICompatibleEmbedder.createEmbeddings(texts, modelToUse, purpose)
 		} catch (error) {
 			TelemetryService.instance.captureEvent(TelemetryEventName.CODE_INDEX_ERROR, {
 				error: error instanceof Error ? error.message : String(error),

src/services/code-index/interfaces/embedder.ts

@@ -1,3 +1,5 @@
+import type { EmbeddingPurpose } from "@roo-code/types"
+
 /**
  * Interface for code index embedders.
  * This interface is implemented by both OpenAI and Ollama embedders.
@@ -7,9 +9,12 @@ export interface IEmbedder {
 	 * Creates embeddings for the given texts.
 	 * @param texts Array of text strings to create embeddings for
 	 * @param model Optional model ID to use for embeddings
+	 * @param purpose Optional embedding purpose ("index" for document indexing, "query" for search queries).
+	 *   When "index", the model's documentPrefix is applied (if any).
+	 *   When "query" or undefined, the model's queryPrefix is applied (if any) for backward compatibility.
 	 * @returns Promise resolving to an EmbeddingResponse
 	 */
-	createEmbeddings(texts: string[], model?: string): Promise<EmbeddingResponse>
+	createEmbeddings(texts: string[], model?: string, purpose?: EmbeddingPurpose): Promise<EmbeddingResponse>
 
 	/**
 	 * Validates the embedder configuration by testing connectivity and credentials.

src/services/code-index/processors/file-watcher.ts

@@ -566,7 +566,7 @@ export class FileWatcher implements IFileWatcher {
 			let pointsToUpsert: PointStruct[] = []
 			if (this.embedder && blocks.length > 0) {
 				const texts = blocks.map((block) => block.content)
-				const { embeddings } = await this.embedder.createEmbeddings(texts)
+				const { embeddings } = await this.embedder.createEmbeddings(texts, undefined, "index")
 
 				pointsToUpsert = blocks.map((block, index) => {
 					const normalizedAbsolutePath = generateNormalizedAbsolutePath(block.file_path, this.workspacePath)

src/services/code-index/processors/scanner.ts

@@ -442,8 +442,8 @@ export class DirectoryScanner implements IDirectoryScanner {
 				}
 				// --- End Deletion Step ---
 
-				// Create embeddings for batch
-				const { embeddings } = await this.embedder.createEmbeddings(batchTexts)
+				// Create embeddings for batch (purpose: "index" for document indexing)
+				const { embeddings } = await this.embedder.createEmbeddings(batchTexts, undefined, "index")
 
 				// Prepare points for Qdrant
 				const points = batchBlocks.map((block, index) => {

src/services/code-index/search-service.ts

@@ -41,8 +41,8 @@ export class CodeIndexSearchService {
 		}
 
 		try {
-			// Generate embedding for query
-			const embeddingResponse = await this.embedder.createEmbeddings([query])
+			// Generate embedding for query (purpose: "query" for search queries)
+			const embeddingResponse = await this.embedder.createEmbeddings([query], undefined, "query")
 			const vector = embeddingResponse?.embeddings[0]
 			if (!vector) {
 				throw new Error("Failed to generate embedding for query.")

src/shared/__tests__/embeddingModels.spec.ts

@@ -2,6 +2,9 @@ import { describe, it, expect } from "vitest"
 import {
 	getModelDimension,
 	getModelScoreThreshold,
+	getModelQueryPrefix,
+	getModelDocumentPrefix,
+	getModelPrefixForPurpose,
 	getDefaultModelId,
 	EMBEDDING_MODEL_PROFILES,
 } from "../embeddingModels"
@@ -92,4 +95,75 @@ describe("embeddingModels", () => {
 			expect(defaultModel).toBe("codestral-embed-2505")
 		})
 	})
+
+	describe("getModelQueryPrefix", () => {
+		it("should return queryPrefix for nomic-embed-code on ollama", () => {
+			const prefix = getModelQueryPrefix("ollama", "nomic-embed-code")
+			expect(prefix).toBe("Represent this query for searching relevant code: ")
+		})
+
+		it("should return queryPrefix for nomic-embed-code on openai-compatible", () => {
+			const prefix = getModelQueryPrefix("openai-compatible", "nomic-embed-code")
+			expect(prefix).toBe("Represent this query for searching relevant code: ")
+		})
+
+		it("should return undefined for models without queryPrefix", () => {
+			const prefix = getModelQueryPrefix("openai", "text-embedding-3-small")
+			expect(prefix).toBeUndefined()
+		})
+
+		it("should return undefined for unknown model", () => {
+			const prefix = getModelQueryPrefix("ollama", "unknown-model")
+			expect(prefix).toBeUndefined()
+		})
+
+		it("should return undefined for unknown provider", () => {
+			const prefix = getModelQueryPrefix("unknown-provider" as any, "some-model")
+			expect(prefix).toBeUndefined()
+		})
+	})
+
+	describe("getModelDocumentPrefix", () => {
+		it("should return undefined for nomic-embed-code on ollama (no prefix for indexing)", () => {
+			const prefix = getModelDocumentPrefix("ollama", "nomic-embed-code")
+			expect(prefix).toBeUndefined()
+		})
+
+		it("should return undefined for nomic-embed-code on openai-compatible (no prefix for indexing)", () => {
+			const prefix = getModelDocumentPrefix("openai-compatible", "nomic-embed-code")
+			expect(prefix).toBeUndefined()
+		})
+
+		it("should return undefined for models without documentPrefix", () => {
+			const prefix = getModelDocumentPrefix("openai", "text-embedding-3-small")
+			expect(prefix).toBeUndefined()
+		})
+
+		it("should return undefined for unknown provider", () => {
+			const prefix = getModelDocumentPrefix("unknown-provider" as any, "some-model")
+			expect(prefix).toBeUndefined()
+		})
+	})
+
+	describe("getModelPrefixForPurpose", () => {
+		it("should return queryPrefix when purpose is 'query'", () => {
+			const prefix = getModelPrefixForPurpose("ollama", "nomic-embed-code", "query")
+			expect(prefix).toBe("Represent this query for searching relevant code: ")
+		})
+
+		it("should return documentPrefix (undefined) when purpose is 'index'", () => {
+			const prefix = getModelPrefixForPurpose("ollama", "nomic-embed-code", "index")
+			expect(prefix).toBeUndefined()
+		})
+
+		it("should fall back to queryPrefix when purpose is undefined (backward compatibility)", () => {
+			const prefix = getModelPrefixForPurpose("ollama", "nomic-embed-code", undefined)
+			expect(prefix).toBe("Represent this query for searching relevant code: ")
+		})
+
+		it("should return undefined for models without any prefix regardless of purpose", () => {
+			expect(getModelPrefixForPurpose("openai", "text-embedding-3-small", "query")).toBeUndefined()
+			expect(getModelPrefixForPurpose("openai", "text-embedding-3-small", "index")).toBeUndefined()
+		})
+	})
 })