fix: add nomic-embed-code support with model-specific score thresholds and query prefixes (#5027)

src/services/code-index/config-manager.ts

@@ -3,7 +3,7 @@ import { ContextProxy } from "../../core/config/ContextProxy"
 import { EmbedderProvider } from "./interfaces/manager"
 import { CodeIndexConfig, PreviousConfigSnapshot } from "./interfaces/config"
 import { SEARCH_MIN_SCORE } from "./constants"
-import { getDefaultModelId, getModelDimension } from "../../shared/embeddingModels"
+import { getDefaultModelId, getModelDimension, getModelScoreThreshold } from "../../shared/embeddingModels"
 
 /**
  * Manages configuration state and validation for the code indexing feature.
@@ -18,7 +18,6 @@ export class CodeIndexConfigManager {
 	private openAiCompatibleOptions?: { baseUrl: string; apiKey: string; modelDimension?: number }
 	private qdrantUrl?: string = "http://localhost:6333"
 	private qdrantApiKey?: string
-	private searchMinScore?: number
 
 	constructor(private readonly contextProxy: ContextProxy) {
 		// Initialize with current configuration to avoid false restart triggers
@@ -61,7 +60,6 @@ export class CodeIndexConfigManager {
 		this.qdrantUrl = codebaseIndexQdrantUrl
 		this.qdrantApiKey = qdrantApiKey ?? ""
 		this.openAiOptions = { openAiNativeApiKey: openAiKey }
-		this.searchMinScore = SEARCH_MIN_SCORE
 
 		// Set embedder provider with support for openai-compatible
 		if (codebaseIndexEmbedderProvider === "ollama") {
@@ -139,7 +137,7 @@ export class CodeIndexConfigManager {
 				openAiCompatibleOptions: this.openAiCompatibleOptions,
 				qdrantUrl: this.qdrantUrl,
 				qdrantApiKey: this.qdrantApiKey,
-				searchMinScore: this.searchMinScore,
+				searchMinScore: this.currentSearchMinScore,
 			},
 			requiresRestart,
 		}
@@ -294,7 +292,7 @@ export class CodeIndexConfigManager {
 			openAiCompatibleOptions: this.openAiCompatibleOptions,
 			qdrantUrl: this.qdrantUrl,
 			qdrantApiKey: this.qdrantApiKey,
-			searchMinScore: this.searchMinScore,
+			searchMinScore: this.currentSearchMinScore,
 		}
 	}
 
@@ -337,9 +335,12 @@ export class CodeIndexConfigManager {
 	}
 
 	/**
-	 * Gets the configured minimum search score.
+	 * Gets the configured minimum search score based on the current model.
+	 * Falls back to the constant SEARCH_MIN_SCORE if no model-specific threshold is found.
 	 */
-	public get currentSearchMinScore(): number | undefined {
-		return this.searchMinScore
+	public get currentSearchMinScore(): number {
+		const currentModelId = this.modelId ?? getDefaultModelId(this.embedderProvider)
+		const modelSpecificThreshold = getModelScoreThreshold(this.embedderProvider, currentModelId)
+		return modelSpecificThreshold ?? SEARCH_MIN_SCORE
 	}
 }

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

@@ -1,5 +1,6 @@
 import { ApiHandlerOptions } from "../../../shared/api"
 import { EmbedderInfo, EmbeddingResponse, IEmbedder } from "../interfaces"
+import { getModelQueryPrefix } from "../../../shared/embeddingModels"
 import { t } from "../../../i18n"
 
 /**
@@ -25,6 +26,10 @@ export class CodeIndexOllamaEmbedder implements IEmbedder {
 		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)
+		const processedTexts = queryPrefix ? texts.map((text) => `${queryPrefix}${text}`) : texts
+
 		try {
 			// Note: Standard Ollama API uses 'prompt' for single text, not 'input' for array.
 			// Implementing based on user's specific request structure.
@@ -35,7 +40,7 @@ export class CodeIndexOllamaEmbedder implements IEmbedder {
 				},
 				body: JSON.stringify({
 					model: modelToUse,
-					input: texts, // Using 'input' as requested
+					input: processedTexts, // Using 'input' as requested
 				}),
 			})
 

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

@@ -6,7 +6,7 @@ import {
 	MAX_BATCH_RETRIES as MAX_RETRIES,
 	INITIAL_RETRY_DELAY_MS as INITIAL_DELAY_MS,
 } from "../constants"
-import { getDefaultModelId } from "../../../shared/embeddingModels"
+import { getDefaultModelId, getModelQueryPrefix } from "../../../shared/embeddingModels"
 import { t } from "../../../i18n"
 
 interface EmbeddingItem {
@@ -59,9 +59,14 @@ export class OpenAICompatibleEmbedder implements IEmbedder {
 	 */
 	async createEmbeddings(texts: string[], model?: string): Promise<EmbeddingResponse> {
 		const modelToUse = model || this.defaultModelId
+
+		// Apply model-specific query prefix if required
+		const queryPrefix = getModelQueryPrefix("openai-compatible", modelToUse)
+		const processedTexts = queryPrefix ? texts.map((text) => `${queryPrefix}${text}`) : texts
+
 		const allEmbeddings: number[][] = []
 		const usage = { promptTokens: 0, totalTokens: 0 }
-		const remainingTexts = [...texts]
+		const remainingTexts = [...processedTexts]
 
 		while (remainingTexts.length > 0) {
 			const currentBatch: string[] = []

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

@@ -8,6 +8,7 @@ import {
 	MAX_BATCH_RETRIES as MAX_RETRIES,
 	INITIAL_RETRY_DELAY_MS as INITIAL_DELAY_MS,
 } from "../constants"
+import { getModelQueryPrefix } from "../../../shared/embeddingModels"
 import { t } from "../../../i18n"
 
 /**
@@ -36,9 +37,14 @@ export class OpenAiEmbedder extends OpenAiNativeHandler implements IEmbedder {
 	 */
 	async createEmbeddings(texts: string[], model?: string): Promise<EmbeddingResponse> {
 		const modelToUse = model || this.defaultModelId
+
+		// Apply model-specific query prefix if required
+		const queryPrefix = getModelQueryPrefix("openai", modelToUse)
+		const processedTexts = queryPrefix ? texts.map((text) => `${queryPrefix}${text}`) : texts
+
 		const allEmbeddings: number[][] = []
 		const usage = { promptTokens: 0, totalTokens: 0 }
-		const remainingTexts = [...texts]
+		const remainingTexts = [...processedTexts]
 
 		while (remainingTexts.length > 0) {
 			const currentBatch: string[] = []

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

@@ -14,7 +14,7 @@ export interface CodeIndexConfig {
 	openAiCompatibleOptions?: { baseUrl: string; apiKey: string; modelDimension?: number }
 	qdrantUrl?: string
 	qdrantApiKey?: string
-	searchMinScore?: number
+	searchMinScore: number
 }
 
 /**

src/shared/embeddingModels.ts

@@ -6,6 +6,8 @@ export type EmbedderProvider = "openai" | "ollama" | "openai-compatible" // Add
 
 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
 	// Add other model-specific properties if needed, e.g., context window size
 }
 
@@ -18,21 +20,31 @@ export type EmbeddingModelProfiles = {
 // Example profiles - expand this list as needed
 export const EMBEDDING_MODEL_PROFILES: EmbeddingModelProfiles = {
 	openai: {
-		"text-embedding-3-small": { dimension: 1536 },
-		"text-embedding-3-large": { dimension: 3072 },
-		"text-embedding-ada-002": { dimension: 1536 },
+		"text-embedding-3-small": { dimension: 1536, scoreThreshold: 0.4 },
+		"text-embedding-3-large": { dimension: 3072, scoreThreshold: 0.4 },
+		"text-embedding-ada-002": { dimension: 1536, scoreThreshold: 0.4 },
 	},
 	ollama: {
-		"nomic-embed-text": { dimension: 768 },
-		"mxbai-embed-large": { dimension: 1024 },
-		"all-minilm": { dimension: 384 },
+		"nomic-embed-text": { dimension: 768, scoreThreshold: 0.4 },
+		"nomic-embed-code": {
+			dimension: 3584,
+			scoreThreshold: 0.15,
+			queryPrefix: "Represent this query for searching relevant code: ",
+		},
+		"mxbai-embed-large": { dimension: 1024, scoreThreshold: 0.4 },
+		"all-minilm": { dimension: 384, scoreThreshold: 0.4 },
 		// Add default Ollama model if applicable, e.g.:
 		// 'default': { dimension: 768 } // Assuming a default dimension
 	},
 	"openai-compatible": {
-		"text-embedding-3-small": { dimension: 1536 },
-		"text-embedding-3-large": { dimension: 3072 },
-		"text-embedding-ada-002": { dimension: 1536 },
+		"text-embedding-3-small": { dimension: 1536, scoreThreshold: 0.4 },
+		"text-embedding-3-large": { dimension: 3072, scoreThreshold: 0.4 },
+		"text-embedding-ada-002": { dimension: 1536, scoreThreshold: 0.4 },
+		"nomic-embed-code": {
+			dimension: 3584,
+			scoreThreshold: 0.15,
+			queryPrefix: "Represent this query for searching relevant code: ",
+		},
 	},
 }
 
@@ -59,6 +71,38 @@ export function getModelDimension(provider: EmbedderProvider, modelId: string):
 	return modelProfile.dimension
 }
 
+/**
+ * Retrieves the score threshold for a given provider and model ID.
+ * @param provider The embedder provider (e.g., "openai").
+ * @param modelId The specific model ID (e.g., "text-embedding-3-small").
+ * @returns The score threshold or undefined if the model is not found.
+ */
+export function getModelScoreThreshold(provider: EmbedderProvider, modelId: string): number | undefined {
+	const providerProfiles = EMBEDDING_MODEL_PROFILES[provider]
+	if (!providerProfiles) {
+		return undefined
+	}
+
+	const modelProfile = providerProfiles[modelId]
+	return modelProfile?.scoreThreshold
+}
+
+/**
+ * Retrieves the query prefix for a given provider and model ID.
+ * @param provider The embedder provider (e.g., "openai").
+ * @param modelId The specific model ID (e.g., "nomic-embed-code").
+ * @returns The query prefix or undefined if the model doesn't require one.
+ */
+export function getModelQueryPrefix(provider: EmbedderProvider, modelId: string): string | undefined {
+	const providerProfiles = EMBEDDING_MODEL_PROFILES[provider]
+	if (!providerProfiles) {
+		return undefined
+	}
+
+	const modelProfile = providerProfiles[modelId]
+	return modelProfile?.queryPrefix
+}
+
 /**
  * Gets the default *specific* embedding model ID based on the provider.
  * Does not include the provider prefix.