fix(knowledge): record embedding usage cost for KB document processing

Adds billing tracking to the KB embedding pipeline, which was previously
generating OpenAI API calls with no cost recorded. Token counts are now
captured from the actual API response and recorded via recordUsage after
successful embedding insertion. BYOK workspaces are excluded from billing.
Applies to all execution paths: direct, BullMQ, and Trigger.dev.

Author: waleedlatif1

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

@@ -77,6 +77,7 @@ vi.stubGlobal(
         { embedding: [0.1, 0.2], index: 0 },
         { embedding: [0.3, 0.4], index: 1 },
       ],
+      usage: { prompt_tokens: 2, total_tokens: 2 },
     }),
   })
 )
@@ -294,7 +295,7 @@ describe('Knowledge Utils', () => {
     it.concurrent('should return same length as input', async () => {
       const result = await generateEmbeddings(['a', 'b'])
 
-      expect(result.length).toBe(2)
+      expect(result.embeddings.length).toBe(2)
     })
 
     it('should use Azure OpenAI when Azure config is provided', async () => {
@@ -313,6 +314,7 @@ describe('Knowledge Utils', () => {
         ok: true,
         json: async () => ({
           data: [{ embedding: [0.1, 0.2], index: 0 }],
+          usage: { prompt_tokens: 1, total_tokens: 1 },
         }),
       } as any)
 
@@ -342,6 +344,7 @@ describe('Knowledge Utils', () => {
         ok: true,
         json: async () => ({
           data: [{ embedding: [0.1, 0.2], index: 0 }],
+          usage: { prompt_tokens: 1, total_tokens: 1 },
         }),
       } as any)
 

apps/sim/lib/billing/core/usage-log.ts

@@ -21,6 +21,7 @@ export type UsageLogSource =
   | 'workspace-chat'
   | 'mcp_copilot'
   | 'mothership_block'
+  | 'knowledge-base'
 
 /**
  * Metadata for 'model' category charges

apps/sim/lib/chunkers/docs-chunker.ts

@@ -81,7 +81,10 @@ export class DocsChunker {
     const textChunks = await this.splitContent(markdownContent)
 
     logger.info(`Generating embeddings for ${textChunks.length} chunks in ${relativePath}`)
-    const embeddings = textChunks.length > 0 ? await generateEmbeddings(textChunks) : []
+    const { embeddings } =
+      textChunks.length > 0
+        ? await generateEmbeddings(textChunks)
+        : { embeddings: [] as number[][] }
     const embeddingModel = 'text-embedding-3-small'
 
     const chunks: DocChunk[] = []

apps/sim/lib/knowledge/chunks/service.ts

@@ -110,7 +110,7 @@ export async function createChunk(
   workspaceId?: string | null
 ): Promise<ChunkData> {
   logger.info(`[${requestId}] Generating embedding for manual chunk`)
-  const embeddings = await generateEmbeddings([chunkData.content], undefined, workspaceId)
+  const { embeddings } = await generateEmbeddings([chunkData.content], undefined, workspaceId)
 
   // Calculate accurate token count
   const tokenCount = estimateTokenCount(chunkData.content, 'openai')
@@ -359,7 +359,7 @@ export async function updateChunk(
       if (content !== currentChunk[0].content) {
         logger.info(`[${requestId}] Content changed, regenerating embedding for chunk ${chunkId}`)
 
-        const embeddings = await generateEmbeddings([content], undefined, workspaceId)
+        const { embeddings } = await generateEmbeddings([content], undefined, workspaceId)
 
         // Calculate accurate token count
         const tokenCount = estimateTokenCount(content, 'openai')

apps/sim/lib/knowledge/documents/service.ts

@@ -25,9 +25,10 @@ import {
   type SQL,
   sql,
 } from 'drizzle-orm'
+import { recordUsage } from '@/lib/billing/core/usage-log'
 import { createBullMQJobData, isBullMQEnabled } from '@/lib/core/bullmq'
 import { env } from '@/lib/core/config/env'
-import { isTriggerDevEnabled } from '@/lib/core/config/feature-flags'
+import { getCostMultiplier, isTriggerDevEnabled } from '@/lib/core/config/feature-flags'
 import { enqueueWorkspaceDispatch } from '@/lib/core/workspace-dispatch'
 import { processDocument } from '@/lib/knowledge/documents/document-processor'
 import type { DocumentSortField, SortOrder } from '@/lib/knowledge/documents/types'
@@ -43,6 +44,7 @@ import type { ProcessedDocumentTags } from '@/lib/knowledge/types'
 import { deleteFile } from '@/lib/uploads/core/storage-service'
 import { extractStorageKey } from '@/lib/uploads/utils/file-utils'
 import type { DocumentProcessingPayload } from '@/background/knowledge-processing'
+import { getEmbeddingModelPricing } from '@/providers/models'
 
 const logger = createLogger('DocumentService')
 
@@ -460,6 +462,9 @@ export async function processDocumentAsync(
       overlap: rawConfig?.overlap ?? 200,
     }
 
+    let totalEmbeddingTokens = 0
+    let embeddingIsBYOK = false
+
     await withTimeout(
       (async () => {
         const processed = await processDocument(
@@ -500,10 +505,16 @@ export async function processDocumentAsync(
             const batchNum = Math.floor(i / batchSize) + 1
 
             logger.info(`[${documentId}] Processing embedding batch ${batchNum}/${totalBatches}`)
-            const batchEmbeddings = await generateEmbeddings(batch, undefined, kb[0].workspaceId)
+            const {
+              embeddings: batchEmbeddings,
+              totalTokens: batchTokens,
+              isBYOK,
+            } = await generateEmbeddings(batch, undefined, kb[0].workspaceId)
             for (const emb of batchEmbeddings) {
               embeddings.push(emb)
             }
+            totalEmbeddingTokens += batchTokens
+            embeddingIsBYOK = isBYOK
           }
         }
 
@@ -638,6 +649,34 @@ export async function processDocumentAsync(
 
     const processingTime = Date.now() - startTime
     logger.info(`[${documentId}] Successfully processed document in ${processingTime}ms`)
+
+    if (!embeddingIsBYOK && totalEmbeddingTokens > 0 && kb[0].userId) {
+      try {
+        const embeddingModel = 'text-embedding-3-small'
+        const pricing = getEmbeddingModelPricing(embeddingModel)
+        if (pricing) {
+          const cost = (totalEmbeddingTokens / 1_000_000) * pricing.input * getCostMultiplier()
+          await recordUsage({
+            userId: kb[0].userId,
+            workspaceId: kb[0].workspaceId ?? undefined,
+            entries: [
+              {
+                category: 'model',
+                source: 'knowledge-base',
+                description: embeddingModel,
+                cost,
+                metadata: { inputTokens: totalEmbeddingTokens, outputTokens: 0 },
+              },
+            ],
+            additionalStats: {
+              totalTokensUsed: sql`total_tokens_used + ${totalEmbeddingTokens}`,
+            },
+          })
+        }
+      } catch (billingError) {
+        logger.error(`[${documentId}] Failed to record embedding usage`, { error: billingError })
+      }
+    }
   } catch (error) {
     const processingTime = Date.now() - startTime
     const errorMessage = error instanceof Error ? error.message : 'Unknown error'

apps/sim/lib/knowledge/embeddings.ts

@@ -35,6 +35,7 @@ interface EmbeddingConfig {
   apiUrl: string
   headers: Record<string, string>
   modelName: string
+  isBYOK: boolean
 }
 
 interface EmbeddingResponseItem {
@@ -71,16 +72,19 @@ async function getEmbeddingConfig(
         'Content-Type': 'application/json',
       },
       modelName: kbModelName,
+      isBYOK: false,
     }
   }
 
   let openaiApiKey = env.OPENAI_API_KEY
+  let isBYOK = false
 
   if (workspaceId) {
     const byokResult = await getBYOKKey(workspaceId, 'openai')
     if (byokResult) {
       logger.info('Using workspace BYOK key for OpenAI embeddings')
       openaiApiKey = byokResult.apiKey
+      isBYOK = true
     }
   }
 
@@ -98,12 +102,16 @@ async function getEmbeddingConfig(
       'Content-Type': 'application/json',
     },
     modelName: embeddingModel,
+    isBYOK,
   }
 }
 
 const EMBEDDING_REQUEST_TIMEOUT_MS = 60_000
 
-async function callEmbeddingAPI(inputs: string[], config: EmbeddingConfig): Promise<number[][]> {
+async function callEmbeddingAPI(
+  inputs: string[],
+  config: EmbeddingConfig
+): Promise<{ embeddings: number[][]; totalTokens: number }> {
   return retryWithExponentialBackoff(
     async () => {
       const useDimensions = supportsCustomDimensions(config.modelName)
@@ -140,7 +148,10 @@ async function callEmbeddingAPI(inputs: string[], config: EmbeddingConfig): Prom
       }
 
       const data: EmbeddingAPIResponse = await response.json()
-      return data.data.map((item) => item.embedding)
+      return {
+        embeddings: data.data.map((item) => item.embedding),
+        totalTokens: data.usage.total_tokens,
+      }
     },
     {
       maxRetries: 3,
@@ -178,14 +189,22 @@ async function processWithConcurrency<T, R>(
   return results
 }
 
+export interface GenerateEmbeddingsResult {
+  embeddings: number[][]
+  totalTokens: number
+  isBYOK: boolean
+}
+
 /**
- * Generate embeddings for multiple texts with token-aware batching and parallel processing
+ * Generate embeddings for multiple texts with token-aware batching and parallel processing.
+ * Returns embeddings alongside the actual token count from the API and whether a BYOK key was used.
+ * Callers should use `totalTokens` and `isBYOK` to record billing via `recordUsage`.
  */
 export async function generateEmbeddings(
   texts: string[],
   embeddingModel = 'text-embedding-3-small',
   workspaceId?: string | null
-): Promise<number[][]> {
+): Promise<GenerateEmbeddingsResult> {
   const config = await getEmbeddingConfig(embeddingModel, workspaceId)
 
   const batches = batchByTokenLimit(texts, MAX_TOKENS_PER_REQUEST, embeddingModel)
@@ -204,13 +223,15 @@ export async function generateEmbeddings(
   )
 
   const allEmbeddings: number[][] = []
+  let totalTokens = 0
   for (const batch of batchResults) {
-    for (const emb of batch) {
+    for (const emb of batch.embeddings) {
       allEmbeddings.push(emb)
     }
+    totalTokens += batch.totalTokens
   }
 
-  return allEmbeddings
+  return { embeddings: allEmbeddings, totalTokens, isBYOK: config.isBYOK }
 }
 
 /**
@@ -227,6 +248,6 @@ export async function generateSearchEmbedding(
     `Using ${config.useAzure ? 'Azure OpenAI' : 'OpenAI'} for search embedding generation`
   )
 
-  const embeddings = await callEmbeddingAPI([query], config)
+  const { embeddings } = await callEmbeddingAPI([query], config)
   return embeddings[0]
 }