update

src/core/rag/embedding.ts

@@ -30,6 +30,7 @@ export const getEmbeddingModel = (
 			return {
 				id: settings.embeddingModelId,
 				dimension: modelInfo.dimensions,
+				supportsBatch: true,
 				getEmbedding: async (text: string) => {
 					try {
 						if (!openai.apiKey) {
@@ -54,6 +55,31 @@ export const getEmbeddingModel = (
 						throw error
 					}
 				},
+				getBatchEmbeddings: async (texts: string[]) => {
+					console.log("use getBatchEmbeddings", texts.length)
+					try {
+						if (!openai.apiKey) {
+							throw new LLMAPIKeyNotSetException(
+								'OpenAI API key is missing. Please set it in settings menu.',
+							)
+						}
+						const embedding = await openai.embeddings.create({
+							model: settings.embeddingModelId,
+							input: texts,
+						})
+						return embedding.data.map(item => item.embedding)
+					} catch (error) {
+						if (
+							error.status === 429 &&
+							error.message.toLowerCase().includes('rate limit')
+						) {
+							throw new LLMRateLimitExceededException(
+								'OpenAI API rate limit exceeded. Please try again later.',
+							)
+						}
+						throw error
+					}
+				},
 			}
 		}
 		case ApiProvider.OpenAI: {
@@ -67,6 +93,7 @@ export const getEmbeddingModel = (
 			return {
 				id: settings.embeddingModelId,
 				dimension: modelInfo.dimensions,
+				supportsBatch: true,
 				getEmbedding: async (text: string) => {
 					try {
 						if (!openai.apiKey) {
@@ -91,6 +118,30 @@ export const getEmbeddingModel = (
 						throw error
 					}
 				},
+				getBatchEmbeddings: async (texts: string[]) => {
+					try {
+						if (!openai.apiKey) {
+							throw new LLMAPIKeyNotSetException(
+								'OpenAI API key is missing. Please set it in settings menu.',
+							)
+						}
+						const embedding = await openai.embeddings.create({
+							model: settings.embeddingModelId,
+							input: texts,
+						})
+						return embedding.data.map(item => item.embedding)
+					} catch (error) {
+						if (
+							error.status === 429 &&
+							error.message.toLowerCase().includes('rate limit')
+						) {
+							throw new LLMRateLimitExceededException(
+								'OpenAI API rate limit exceeded. Please try again later.',
+							)
+						}
+						throw error
+					}
+				},
 			}
 		}
 		case ApiProvider.SiliconFlow: {
@@ -104,6 +155,7 @@ export const getEmbeddingModel = (
 			return {
 				id: settings.embeddingModelId,
 				dimension: modelInfo.dimensions,
+				supportsBatch: true,
 				getEmbedding: async (text: string) => {
 					try {
 						if (!openai.apiKey) {
@@ -128,6 +180,30 @@ export const getEmbeddingModel = (
 						throw error
 					}
 				},
+				getBatchEmbeddings: async (texts: string[]) => {
+					try {
+						if (!openai.apiKey) {
+							throw new LLMAPIKeyNotSetException(
+								'SiliconFlow API key is missing. Please set it in settings menu.',
+							)
+						}
+						const embedding = await openai.embeddings.create({
+							model: settings.embeddingModelId,
+							input: texts,
+						})
+						return embedding.data.map(item => item.embedding)
+					} catch (error) {
+						if (
+							error.status === 429 &&
+							error.message.toLowerCase().includes('rate limit')
+						) {
+							throw new LLMRateLimitExceededException(
+								'SiliconFlow API rate limit exceeded. Please try again later.',
+							)
+						}
+						throw error
+					}
+				},
 			}
 		}
 		case ApiProvider.AlibabaQwen: {
@@ -141,6 +217,7 @@ export const getEmbeddingModel = (
 			return {
 				id: settings.embeddingModelId,
 				dimension: modelInfo.dimensions,
+				supportsBatch: false,
 				getEmbedding: async (text: string) => {
 					try {
 						if (!openai.apiKey) {
@@ -165,6 +242,30 @@ export const getEmbeddingModel = (
 						throw error
 					}
 				},
+				getBatchEmbeddings: async (texts: string[]) => {
+					try {
+						if (!openai.apiKey) {
+							throw new LLMAPIKeyNotSetException(
+								'Alibaba Qwen API key is missing. Please set it in settings menu.',
+							)
+						}
+						const embedding = await openai.embeddings.create({
+							model: settings.embeddingModelId,
+							input: texts,
+						})
+						return embedding.data.map(item => item.embedding)
+					} catch (error) {
+						if (
+							error.status === 429 &&
+							error.message.toLowerCase().includes('rate limit')
+						) {
+							throw new LLMRateLimitExceededException(
+								'Alibaba Qwen API rate limit exceeded. Please try again later.',
+							)
+						}
+						throw error
+					}
+				},
 			}
 		}
 		case ApiProvider.Google: {
@@ -174,6 +275,7 @@ export const getEmbeddingModel = (
 			return {
 				id: settings.embeddingModelId,
 				dimension: modelInfo.dimensions,
+				supportsBatch: false,
 				getEmbedding: async (text: string) => {
 					try {
 						const response = await model.embedContent(text)
@@ -190,6 +292,27 @@ export const getEmbeddingModel = (
 						throw error
 					}
 				},
+				getBatchEmbeddings: async (texts: string[]) => {
+					try {
+						const embeddings = await Promise.all(
+							texts.map(async (text) => {
+								const response = await model.embedContent(text)
+								return response.embedding.values
+							})
+						)
+						return embeddings
+					} catch (error) {
+						if (
+							error.status === 429 &&
+							error.message.includes('RATE_LIMIT_EXCEEDED')
+						) {
+							throw new LLMRateLimitExceededException(
+								'Gemini API rate limit exceeded. Please try again later.',
+							)
+						}
+						throw error
+					}
+				},
 			}
 		}
 		case ApiProvider.Ollama: {
@@ -201,6 +324,7 @@ export const getEmbeddingModel = (
 			return {
 				id: settings.embeddingModelId,
 				dimension: 0,
+				supportsBatch: false,
 				getEmbedding: async (text: string) => {
 					if (!settings.ollamaProvider.baseUrl) {
 						throw new LLMBaseUrlNotSetException(
@@ -213,6 +337,18 @@ export const getEmbeddingModel = (
 					})
 					return embedding.data[0].embedding
 				},
+				getBatchEmbeddings: async (texts: string[]) => {
+					if (!settings.ollamaProvider.baseUrl) {
+						throw new LLMBaseUrlNotSetException(
+							'Ollama Address is missing. Please set it in settings menu.',
+						)
+					}
+					const embedding = await openai.embeddings.create({
+						model: settings.embeddingModelId,
+						input: texts,
+					})
+					return embedding.data.map(item => item.embedding)
+				},
 			}
 		}
 		case ApiProvider.OpenAICompatible: {
@@ -224,6 +360,7 @@ export const getEmbeddingModel = (
 			return {
 				id: settings.embeddingModelId,
 				dimension: 0,
+				supportsBatch: false,
 				getEmbedding: async (text: string) => {
 					try {
 						if (!openai.apiKey) {
@@ -249,6 +386,31 @@ export const getEmbeddingModel = (
 						throw error
 					}
 				},
+				getBatchEmbeddings: async (texts: string[]) => {
+					try {
+						if (!openai.apiKey) {
+							throw new LLMAPIKeyNotSetException(
+								'OpenAI Compatible API key is missing. Please set it in settings menu.',
+							)
+						}
+						const embedding = await openai.embeddings.create({
+							model: settings.embeddingModelId,
+							input: texts,
+							encoding_format: "float",
+						})
+						return embedding.data.map(item => item.embedding)
+					} catch (error) {
+						if (
+							error.status === 429 &&
+							error.message.toLowerCase().includes('rate limit')
+						) {
+							throw new LLMRateLimitExceededException(
+								'OpenAI Compatible API rate limit exceeded. Please try again later.',
+							)
+						}
+						throw error
+					}
+				},
 			}
 		}
 		default:

src/database/modules/vector/vector-manager.ts

@@ -131,27 +131,34 @@ export class VectorManager {
 
 		const embeddingProgress = { completed: 0 }
 		const embeddingChunks: InsertVector[] = []
-		const batchSize = 100
+		const insertBatchSize = 100 // 数据库插入批量大小
-		const limit = pLimit(50)
+		
-		const abortController = new AbortController()
+		try {
-		const tasks = contentChunks.map((chunk) =>
+			if (embeddingModel.supportsBatch) {
-			limit(async () => {
+				// 支持批量处理的提供商：使用批量处理逻辑
-				if (abortController.signal.aborted) {
+				const embeddingBatchSize = 100 // API批量处理大小
-					throw new Error('Operation was aborted')
+				
-				}
+				for (let i = 0; i < contentChunks.length; i += embeddingBatchSize) {
-				try {
+					const batchChunks = contentChunks.slice(i, Math.min(i + embeddingBatchSize, contentChunks.length))
+					const batchTexts = batchChunks.map(chunk => chunk.content)
+					
 					await backOff(
 						async () => {
-							const embedding = await embeddingModel.getEmbedding(chunk.content)
+							const batchEmbeddings = await embeddingModel.getBatchEmbeddings(batchTexts)
-							const embeddedChunk = {
+							
-								path: chunk.path,
+							// 合并embedding结果到chunk数据
-								mtime: chunk.mtime,
+							for (let j = 0; j < batchChunks.length; j++) {
-								content: chunk.content,
+								const embeddedChunk: InsertVector = {
-								embedding,
+									path: batchChunks[j].path,
-								metadata: chunk.metadata,
+									mtime: batchChunks[j].mtime,
+									content: batchChunks[j].content,
+									embedding: batchEmbeddings[j],
+									metadata: batchChunks[j].metadata,
+								}
+								embeddingChunks.push(embeddedChunk)
 							}
-							embeddingChunks.push(embeddedChunk)
+							
-							embeddingProgress.completed++
+							embeddingProgress.completed += batchChunks.length
 							updateProgress?.({
 								completedChunks: embeddingProgress.completed,
 								totalChunks: contentChunks.length,
@@ -165,15 +172,51 @@ export class VectorManager {
 							jitter: 'full',
 						},
 					)
-				} catch (error) {
-					abortController.abort()
-					throw error
 				}
-			}),
+			} else {
-		)
+				// 不支持批量处理的提供商：使用原来的逐个处理逻辑
-
+				const limit = pLimit(50)
-		try {
+				const abortController = new AbortController()
-			await Promise.all(tasks)
+				const tasks = contentChunks.map((chunk) =>
+					limit(async () => {
+						if (abortController.signal.aborted) {
+							throw new Error('Operation was aborted')
+						}
+						try {
+							await backOff(
+								async () => {
+									const embedding = await embeddingModel.getEmbedding(chunk.content)
+									const embeddedChunk = {
+										path: chunk.path,
+										mtime: chunk.mtime,
+										content: chunk.content,
+										embedding,
+										metadata: chunk.metadata,
+									}
+									embeddingChunks.push(embeddedChunk)
+									embeddingProgress.completed++
+									updateProgress?.({
+										completedChunks: embeddingProgress.completed,
+										totalChunks: contentChunks.length,
+										totalFiles: filesToIndex.length,
+									})
+								},
+								{
+									numOfAttempts: 5,
+									startingDelay: 1000,
+									timeMultiple: 1.5,
+									jitter: 'full',
+								},
+							)
+						} catch (error) {
+							abortController.abort()
+							throw error
+						}
+					}),
+				)
+				
+				await Promise.all(tasks)
+			}
 
 			// all embedding generated, batch insert
 			if (embeddingChunks.length > 0) {
@@ -182,7 +225,7 @@ export class VectorManager {
 				while (inserted < embeddingChunks.length) {
 					const chunksToInsert = embeddingChunks.slice(
 						inserted,
-						Math.min(inserted + batchSize, embeddingChunks.length)
+						Math.min(inserted + insertBatchSize, embeddingChunks.length)
 					)
 					await this.repository.insertVectors(chunksToInsert, embeddingModel)
 					inserted += chunksToInsert.length
@@ -242,25 +285,32 @@ export class VectorManager {
 		})
 
 		const embeddingChunks: InsertVector[] = []
-		const limit = pLimit(50)
+		const insertBatchSize = 100 // 数据库插入批量大小
-		const abortController = new AbortController()
+		
-		const tasks = contentChunks.map((chunk) =>
+		try {
-			limit(async () => {
+			if (embeddingModel.supportsBatch) {
-				if (abortController.signal.aborted) {
+				// 支持批量处理的提供商：使用批量处理逻辑
-					throw new Error('Operation was aborted')
+				const embeddingBatchSize = 100 // API批量处理大小
-				}
+				
-				try {
+				for (let i = 0; i < contentChunks.length; i += embeddingBatchSize) {
+					const batchChunks = contentChunks.slice(i, Math.min(i + embeddingBatchSize, contentChunks.length))
+					const batchTexts = batchChunks.map(chunk => chunk.content)
+					
 					await backOff(
 						async () => {
-							const embedding = await embeddingModel.getEmbedding(chunk.content)
+							const batchEmbeddings = await embeddingModel.getBatchEmbeddings(batchTexts)
-							const embeddedChunk = {
+							
-								path: chunk.path,
+							// 合并embedding结果到chunk数据
-								mtime: chunk.mtime,
+							for (let j = 0; j < batchChunks.length; j++) {
-								content: chunk.content,
+								const embeddedChunk: InsertVector = {
-								embedding,
+									path: batchChunks[j].path,
-								metadata: chunk.metadata,
+									mtime: batchChunks[j].mtime,
+									content: batchChunks[j].content,
+									embedding: batchEmbeddings[j],
+									metadata: batchChunks[j].metadata,
+								}
+								embeddingChunks.push(embeddedChunk)
 							}
-							embeddingChunks.push(embeddedChunk)
 						},
 						{
 							numOfAttempts: 5,
@@ -269,22 +319,51 @@ export class VectorManager {
 							jitter: 'full',
 						},
 					)
-				} catch (error) {
-					abortController.abort()
-					throw error
 				}
-			}),
+			} else {
-		)
+				// 不支持批量处理的提供商：使用原来的逐个处理逻辑
-
+				const limit = pLimit(50)
-		try {
+				const abortController = new AbortController()
-			await Promise.all(tasks)
+				const tasks = contentChunks.map((chunk) =>
+					limit(async () => {
+						if (abortController.signal.aborted) {
+							throw new Error('Operation was aborted')
+						}
+						try {
+							await backOff(
+								async () => {
+									const embedding = await embeddingModel.getEmbedding(chunk.content)
+									const embeddedChunk = {
+										path: chunk.path,
+										mtime: chunk.mtime,
+										content: chunk.content,
+										embedding,
+										metadata: chunk.metadata,
+									}
+									embeddingChunks.push(embeddedChunk)
+								},
+								{
+									numOfAttempts: 5,
+									startingDelay: 1000,
+									timeMultiple: 1.5,
+									jitter: 'full',
+								},
+							)
+						} catch (error) {
+							abortController.abort()
+							throw error
+						}
+					}),
+				)
+				
+				await Promise.all(tasks)
+			}
 
 			// all embedding generated, batch insert
 			if (embeddingChunks.length > 0) {
-				const batchSize = 100
 				let inserted = 0
 				while (inserted < embeddingChunks.length) {
-					const chunksToInsert = embeddingChunks.slice(inserted, Math.min(inserted + batchSize, embeddingChunks.length))
+					const chunksToInsert = embeddingChunks.slice(inserted, Math.min(inserted + insertBatchSize, embeddingChunks.length))
 					await this.repository.insertVectors(chunksToInsert, embeddingModel)
 					inserted += chunksToInsert.length
 				}

src/types/embedding.ts

@@ -17,5 +17,7 @@ export type EmbeddingModelOption = {
 export type EmbeddingModel = {
   id: string
   dimension: number
+  supportsBatch: boolean
   getEmbedding: (text: string) => Promise<number[]>
+  getBatchEmbeddings: (texts: string[]) => Promise<number[][]>
 }