fix(kb): added tiktoken for embedding token estimation

apps/sim/app/api/files/upload/route.ts

@@ -9,23 +9,22 @@ import {
   InvalidRequestError,
 } from '@/app/api/files/utils'
 
-// Allowlist of permitted file extensions for security
 const ALLOWED_EXTENSIONS = new Set([
-  // Documents
   'pdf',
   'doc',
   'docx',
   'txt',
   'md',
-  // Images (safe formats)
   'png',
   'jpg',
   'jpeg',
   'gif',
-  // Data files
   'csv',
   'xlsx',
   'xls',
+  'json',
+  'yaml',
+  'yml',
 ])
 
 /**
@@ -50,19 +49,16 @@ export async function POST(request: NextRequest) {
 
     const formData = await request.formData()
 
-    // Check if multiple files are being uploaded or a single file
     const files = formData.getAll('file') as File[]
 
     if (!files || files.length === 0) {
       throw new InvalidRequestError('No files provided')
     }
 
-    // Get optional scoping parameters for execution-scoped storage
     const workflowId = formData.get('workflowId') as string | null
     const executionId = formData.get('executionId') as string | null
     const workspaceId = formData.get('workspaceId') as string | null
 
-    // Log storage mode
     const usingCloudStorage = isUsingCloudStorage()
     logger.info(`Using storage mode: ${usingCloudStorage ? 'Cloud' : 'Local'} for file upload`)
 
@@ -74,7 +70,6 @@ export async function POST(request: NextRequest) {
 
     const uploadResults = []
 
-    // Process each file
     for (const file of files) {
       const originalName = file.name
 
@@ -88,9 +83,7 @@ export async function POST(request: NextRequest) {
       const bytes = await file.arrayBuffer()
       const buffer = Buffer.from(bytes)
 
-      // For execution-scoped files, use the dedicated execution file storage
       if (workflowId && executionId) {
-        // Use the dedicated execution file storage system
         const { uploadExecutionFile } = await import('@/lib/workflows/execution-file-storage')
         const userFile = await uploadExecutionFile(
           {
@@ -107,13 +100,10 @@ export async function POST(request: NextRequest) {
         continue
       }
 
-      // Upload to cloud or local storage using the standard uploadFile function
       try {
         logger.info(`Uploading file: ${originalName}`)
         const result = await uploadFile(buffer, originalName, file.type, file.size)
 
-        // Generate a presigned URL for cloud storage with appropriate expiry
-        // Regular files get 24 hours (execution files are handled above)
         let presignedUrl: string | undefined
         if (usingCloudStorage) {
           try {
@@ -144,7 +134,6 @@ export async function POST(request: NextRequest) {
       }
     }
 
-    // Return all file information
     if (uploadResults.length === 1) {
       return NextResponse.json(uploadResults[0])
     }
@@ -155,7 +144,6 @@ export async function POST(request: NextRequest) {
   }
 }
 
-// Handle preflight requests
 export async function OPTIONS() {
   return createOptionsResponse()
 }

apps/sim/app/api/knowledge/search/utils.test.ts

@@ -32,6 +32,7 @@ vi.stubGlobal(
 
 vi.mock('@/lib/env', () => ({
   env: {},
+  getEnv: (key: string) => process.env[key],
   isTruthy: (value: string | boolean | number | undefined) =>
     typeof value === 'string' ? value === 'true' || value === '1' : Boolean(value),
 }))

apps/sim/app/api/knowledge/utils.test.ts

@@ -17,6 +17,7 @@ vi.mock('drizzle-orm', () => ({
 
 vi.mock('@/lib/env', () => ({
   env: { OPENAI_API_KEY: 'test-key' },
+  getEnv: (key: string) => process.env[key],
   isTruthy: (value: string | boolean | number | undefined) =>
     typeof value === 'string' ? value === 'true' || value === '1' : Boolean(value),
 }))

apps/sim/lib/chunkers/json-yaml-chunker.ts

@@ -1,18 +1,29 @@
+import * as yaml from 'js-yaml'
+import { createLogger } from '@/lib/logs/console/logger'
+import { getAccurateTokenCount } from '@/lib/tokenization'
 import { estimateTokenCount } from '@/lib/tokenization/estimators'
 import type { Chunk, ChunkerOptions } from './types'
 
+const logger = createLogger('JsonYamlChunker')
+
 function getTokenCount(text: string): number {
-  const estimate = estimateTokenCount(text)
-  return estimate.count
+  try {
+    return getAccurateTokenCount(text, 'text-embedding-3-small')
+  } catch (error) {
+    logger.warn('Tiktoken failed, falling back to estimation')
+    const estimate = estimateTokenCount(text)
+    return estimate.count
+  }
 }
 
 /**
  * Configuration for JSON/YAML chunking
+ * Reduced limits to ensure we stay well under OpenAI's 8,191 token limit per embedding request
  */
 const JSON_YAML_CHUNKING_CONFIG = {
-  TARGET_CHUNK_SIZE: 2000, // Target tokens per chunk
+  TARGET_CHUNK_SIZE: 1000, // Target tokens per chunk
   MIN_CHUNK_SIZE: 100, // Minimum tokens per chunk
-  MAX_CHUNK_SIZE: 3000, // Maximum tokens per chunk
+  MAX_CHUNK_SIZE: 1500, // Maximum tokens per chunk
   MAX_DEPTH_FOR_SPLITTING: 5, // Maximum depth to traverse for splitting
 }
 
@@ -34,7 +45,6 @@ export class JsonYamlChunker {
       return true
     } catch {
       try {
-        const yaml = require('js-yaml')
         yaml.load(content)
         return true
       } catch {
@@ -48,9 +58,26 @@ export class JsonYamlChunker {
    */
   async chunk(content: string): Promise<Chunk[]> {
     try {
-      const data = JSON.parse(content)
-      return this.chunkStructuredData(data)
+      let data: any
+      try {
+        data = JSON.parse(content)
+      } catch {
+        data = yaml.load(content)
+      }
+      const chunks = this.chunkStructuredData(data)
+
+      const tokenCounts = chunks.map((c) => c.tokenCount)
+      const totalTokens = tokenCounts.reduce((a, b) => a + b, 0)
+      const maxTokens = Math.max(...tokenCounts)
+      const avgTokens = Math.round(totalTokens / chunks.length)
+
+      logger.info(
+        `JSON chunking complete: ${chunks.length} chunks, ${totalTokens} total tokens (avg: ${avgTokens}, max: ${maxTokens})`
+      )
+
+      return chunks
     } catch (error) {
+      logger.info('JSON parsing failed, falling back to text chunking')
       return this.chunkAsText(content)
     }
   }
@@ -102,7 +129,6 @@ export class JsonYamlChunker {
       const itemTokens = getTokenCount(itemStr)
 
       if (itemTokens > this.chunkSize) {
-        // Save current batch if it has items
         if (currentBatch.length > 0) {
           const batchContent = contextHeader + JSON.stringify(currentBatch, null, 2)
           chunks.push({
@@ -134,7 +160,7 @@ export class JsonYamlChunker {
         const batchContent = contextHeader + JSON.stringify(currentBatch, null, 2)
         chunks.push({
           text: batchContent,
-          tokenCount: currentTokens,
+          tokenCount: getTokenCount(batchContent),
           metadata: {
             startIndex: i - currentBatch.length,
             endIndex: i - 1,
@@ -152,7 +178,7 @@ export class JsonYamlChunker {
       const batchContent = contextHeader + JSON.stringify(currentBatch, null, 2)
       chunks.push({
         text: batchContent,
-        tokenCount: currentTokens,
+        tokenCount: getTokenCount(batchContent),
         metadata: {
           startIndex: arr.length - currentBatch.length,
           endIndex: arr.length - 1,
@@ -194,12 +220,11 @@ export class JsonYamlChunker {
       const valueTokens = getTokenCount(valueStr)
 
       if (valueTokens > this.chunkSize) {
-        // Save current object if it has properties
         if (Object.keys(currentObj).length > 0) {
           const objContent = JSON.stringify(currentObj, null, 2)
           chunks.push({
             text: objContent,
-            tokenCount: currentTokens,
+            tokenCount: getTokenCount(objContent),
             metadata: {
               startIndex: 0,
               endIndex: objContent.length,
@@ -230,7 +255,7 @@ export class JsonYamlChunker {
         const objContent = JSON.stringify(currentObj, null, 2)
         chunks.push({
           text: objContent,
-          tokenCount: currentTokens,
+          tokenCount: getTokenCount(objContent),
           metadata: {
             startIndex: 0,
             endIndex: objContent.length,
@@ -250,7 +275,7 @@ export class JsonYamlChunker {
       const objContent = JSON.stringify(currentObj, null, 2)
       chunks.push({
         text: objContent,
-        tokenCount: currentTokens,
+        tokenCount: getTokenCount(objContent),
         metadata: {
           startIndex: 0,
           endIndex: objContent.length,
@@ -262,7 +287,7 @@ export class JsonYamlChunker {
   }
 
   /**
-   * Fall back to text chunking if JSON parsing fails.
+   * Fall back to text chunking if JSON parsing fails
    */
   private async chunkAsText(content: string): Promise<Chunk[]> {
     const chunks: Chunk[] = []
@@ -308,7 +333,7 @@ export class JsonYamlChunker {
   }
 
   /**
-   * Static method for chunking JSON/YAML data with default options.
+   * Static method for chunking JSON/YAML data with default options
    */
   static async chunkJsonYaml(content: string, options: ChunkerOptions = {}): Promise<Chunk[]> {
     const chunker = new JsonYamlChunker(options)

apps/sim/lib/embeddings/utils.ts

@@ -1,9 +1,12 @@
 import { env } from '@/lib/env'
 import { isRetryableError, retryWithExponentialBackoff } from '@/lib/knowledge/documents/utils'
 import { createLogger } from '@/lib/logs/console/logger'
+import { batchByTokenLimit, getTotalTokenCount } from '@/lib/tokenization'
 
 const logger = createLogger('EmbeddingUtils')
 
+const MAX_TOKENS_PER_REQUEST = 8000
+
 export class EmbeddingAPIError extends Error {
   public status: number
 
@@ -104,35 +107,54 @@ async function callEmbeddingAPI(inputs: string[], config: EmbeddingConfig): Prom
 }
 
 /**
- * Generate embeddings for multiple texts with simple batching
+ * Generate embeddings for multiple texts with token-aware batching
+ * Uses tiktoken for token counting
  */
 export async function generateEmbeddings(
   texts: string[],
   embeddingModel = 'text-embedding-3-small'
 ): Promise<number[][]> {
   const config = getEmbeddingConfig(embeddingModel)
 
-  logger.info(`Using ${config.useAzure ? 'Azure OpenAI' : 'OpenAI'} for embeddings generation`)
+  logger.info(
+    `Using ${config.useAzure ? 'Azure OpenAI' : 'OpenAI'} for embeddings generation (${texts.length} texts)`
+  )
+
+  const batches = batchByTokenLimit(texts, MAX_TOKENS_PER_REQUEST, embeddingModel)
+
+  logger.info(
+    `Split ${texts.length} texts into ${batches.length} batches (max ${MAX_TOKENS_PER_REQUEST} tokens per batch)`
+  )
 
-  // Reduced batch size to prevent API timeouts and improve reliability
-  const batchSize = 50 // Reduced from 100 to prevent issues with large documents
   const allEmbeddings: number[][] = []
 
-  for (let i = 0; i < texts.length; i += batchSize) {
-    const batch = texts.slice(i, i + batchSize)
-    const batchEmbeddings = await callEmbeddingAPI(batch, config)
-    allEmbeddings.push(...batchEmbeddings)
+  for (let i = 0; i < batches.length; i++) {
+    const batch = batches[i]
+    const batchTokenCount = getTotalTokenCount(batch, embeddingModel)
 
     logger.info(
-      `Generated embeddings for batch ${Math.floor(i / batchSize) + 1}/${Math.ceil(texts.length / batchSize)}`
+      `Processing batch ${i + 1}/${batches.length}: ${batch.length} texts, ${batchTokenCount} tokens`
     )
 
-    // Add small delay between batches to avoid rate limiting
-    if (i + batchSize < texts.length) {
+    try {
+      const batchEmbeddings = await callEmbeddingAPI(batch, config)
+      allEmbeddings.push(...batchEmbeddings)
+
+      logger.info(
+        `Generated ${batchEmbeddings.length} embeddings for batch ${i + 1}/${batches.length}`
+      )
+    } catch (error) {
+      logger.error(`Failed to generate embeddings for batch ${i + 1}:`, error)
+      throw error
+    }
+
+    if (i + 1 < batches.length) {
       await new Promise((resolve) => setTimeout(resolve, 100))
     }
   }
 
+  logger.info(`Successfully generated ${allEmbeddings.length} embeddings total`)
+
   return allEmbeddings
 }