Handle llm setting errors during embedding generation (#51)

src/components/chat-view/Chat.tsx

@@ -19,7 +19,6 @@ import { useLLM } from '../../contexts/llm-context'
 import { useRAG } from '../../contexts/rag-context'
 import { useSettings } from '../../contexts/settings-context'
 import { useChatHistory } from '../../hooks/useChatHistory'
-import { OpenSettingsModal } from '../../OpenSettingsModal'
 import { ChatMessage, ChatUserMessage } from '../../types/chat'
 import {
   MentionableBlock,
@@ -28,15 +27,16 @@ import {
 } from '../../types/mentionable'
 import { applyChangesToFile } from '../../utils/apply'
 import {
-  LLMABaseUrlNotSetException,
   LLMAPIKeyInvalidException,
   LLMAPIKeyNotSetException,
+  LLMBaseUrlNotSetException,
 } from '../../utils/llm/exception'
 import {
   getMentionableKey,
   serializeMentionable,
 } from '../../utils/mentionable'
 import { readTFileContent } from '../../utils/obsidian'
+import { openSettingsModalWithError } from '../../utils/openSettingsModal'
 import { PromptGenerator } from '../../utils/promptGenerator'
 
 import ChatUserInput, { ChatUserInputRef } from './chat-input/ChatUserInput'
@@ -261,14 +261,9 @@ const Chat = forwardRef<ChatRef, ChatProps>((props, ref) => {
       if (
         error instanceof LLMAPIKeyNotSetException ||
         error instanceof LLMAPIKeyInvalidException ||
-        error instanceof LLMABaseUrlNotSetException
+        error instanceof LLMBaseUrlNotSetException
       ) {
-        new OpenSettingsModal(app, error.message, () => {
-          // eslint-disable-next-line @typescript-eslint/no-explicit-any
-          const setting = (app as any).setting
-          setting.open()
-          setting.openTabById('smart-composer')
-        }).open()
+        openSettingsModalWithError(app, error.message)
       } else {
         new Notice(error.message)
         console.error('Failed to generate response', error)
@@ -324,14 +319,10 @@ const Chat = forwardRef<ChatRef, ChatProps>((props, ref) => {
     onError: (error) => {
       if (
         error instanceof LLMAPIKeyNotSetException ||
-        error instanceof LLMAPIKeyInvalidException
+        error instanceof LLMAPIKeyInvalidException ||
+        error instanceof LLMBaseUrlNotSetException
       ) {
-        new OpenSettingsModal(app, error.message, () => {
-          // eslint-disable-next-line @typescript-eslint/no-explicit-any
-          const setting = (app as any).setting
-          setting.open()
-          setting.openTabById('smart-composer')
-        }).open()
+        openSettingsModalWithError(app, error.message)
       } else {
         new Notice(error.message)
         console.error('Failed to apply changes', error)

src/utils/embedding.ts

@@ -2,6 +2,10 @@ import { OpenAI } from 'openai'
 
 import { EmbeddingModel } from '../types/embedding'
 
+import {
+  LLMAPIKeyNotSetException,
+  LLMBaseUrlNotSetException,
+} from './llm/exception'
 import { NoStainlessOpenAI } from './llm/ollama'
 
 export const getEmbeddingModel = (
@@ -21,6 +25,11 @@ export const getEmbeddingModel = (
         name: 'text-embedding-3-small',
         dimension: 1536,
         getEmbedding: async (text: string) => {
+          if (!openai.apiKey) {
+            throw new LLMAPIKeyNotSetException(
+              'OpenAI API key is missing. Please set it in settings menu.',
+            )
+          }
           const embedding = await openai.embeddings.create({
             model: 'text-embedding-3-small',
             input: text,
@@ -38,6 +47,11 @@ export const getEmbeddingModel = (
         name: 'text-embedding-3-large',
         dimension: 3072,
         getEmbedding: async (text: string) => {
+          if (!openai.apiKey) {
+            throw new LLMAPIKeyNotSetException(
+              'OpenAI API key is missing. Please set it in settings menu.',
+            )
+          }
           const embedding = await openai.embeddings.create({
             model: 'text-embedding-3-large',
             input: text,
@@ -56,6 +70,11 @@ export const getEmbeddingModel = (
         name: 'nomic-embed-text',
         dimension: 768,
         getEmbedding: async (text: string) => {
+          if (!ollamaBaseUrl) {
+            throw new LLMBaseUrlNotSetException(
+              'Ollama Address is missing. Please set it in settings menu.',
+            )
+          }
           const embedding = await openai.embeddings.create({
             model: 'nomic-embed-text',
             input: text,
@@ -74,6 +93,11 @@ export const getEmbeddingModel = (
         name: 'mxbai-embed-large',
         dimension: 1024,
         getEmbedding: async (text: string) => {
+          if (!ollamaBaseUrl) {
+            throw new LLMBaseUrlNotSetException(
+              'Ollama Address is missing. Please set it in settings menu.',
+            )
+          }
           const embedding = await openai.embeddings.create({
             model: 'mxbai-embed-large',
             input: text,

src/utils/llm/exception.ts

@@ -12,9 +12,9 @@ export class LLMAPIKeyInvalidException extends Error {
   }
 }
 
-export class LLMABaseUrlNotSetException extends Error {
+export class LLMBaseUrlNotSetException extends Error {
   constructor(message: string) {
     super(message)
-    this.name = 'LLMABaseUrlNotSetException'
+    this.name = 'LLMBaseUrlNotSetException'
   }
 }

src/utils/llm/ollama.ts

@@ -11,7 +11,7 @@ import {
 } from 'src/types/llm/response'
 
 import { BaseLLMProvider } from './base'
-import { LLMABaseUrlNotSetException } from './exception'
+import { LLMBaseUrlNotSetException } from './exception'
 import { OpenAICompatibleProvider } from './openaiCompatibleProvider'
 
 export class NoStainlessOpenAI extends OpenAI {
@@ -59,7 +59,7 @@ export class OllamaOpenAIProvider implements BaseLLMProvider {
     options?: LLMOptions,
   ): Promise<LLMResponseNonStreaming> {
     if (!this.ollamaBaseUrl) {
-      throw new LLMABaseUrlNotSetException(
+      throw new LLMBaseUrlNotSetException(
         'Ollama Address is missing. Please set it in settings menu.',
       )
     }
@@ -70,7 +70,7 @@ export class OllamaOpenAIProvider implements BaseLLMProvider {
     options?: LLMOptions,
   ): Promise<AsyncIterable<LLMResponseStreaming>> {
     if (!this.ollamaBaseUrl) {
-      throw new LLMABaseUrlNotSetException(
+      throw new LLMBaseUrlNotSetException(
         'Ollama Address is missing. Please set it in settings menu.',
       )
     }

src/utils/openSettingsModal.ts

@@ -0,0 +1,12 @@
+import { App } from 'obsidian'
+
+import { OpenSettingsModal } from '../OpenSettingsModal'
+
+export function openSettingsModalWithError(app: App, errorMessage: string) {
+  new OpenSettingsModal(app, errorMessage, () => {
+    // eslint-disable-next-line @typescript-eslint/no-explicit-any
+    const setting = (app as any).setting
+    setting.open()
+    setting.openTabById('smart-composer')
+  }).open()
+}

src/utils/vector-db/manager.ts

@@ -7,6 +7,12 @@ import { IndexProgress } from '../../components/chat-view/QueryProgress'
 import { PGLITE_DB_PATH } from '../../constants'
 import { InsertVector, SelectVector } from '../../db/schema'
 import { EmbeddingModel } from '../../types/embedding'
+import {
+  LLMAPIKeyInvalidException,
+  LLMAPIKeyNotSetException,
+  LLMBaseUrlNotSetException,
+} from '../llm/exception'
+import { openSettingsModalWithError } from '../openSettingsModal'
 
 import { VectorDbRepository } from './repository'
 
@@ -121,64 +127,81 @@ export class VectorDbManager {
     const embeddingChunks: InsertVector[] = []
     const batchSize = 100
     const limit = pLimit(50)
+    const abortController = new AbortController()
     const tasks = contentChunks.map((chunk) =>
       limit(async () => {
-        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,
-            })
-
-            // Insert vectors in batches
-            if (
-              embeddingChunks.length >=
-                embeddingProgress.inserted + batchSize ||
-              embeddingChunks.length === contentChunks.length
-            ) {
-              await this.repository.insertVectors(
-                embeddingChunks.slice(
-                  embeddingProgress.inserted,
-                  embeddingProgress.inserted + batchSize,
-                ),
-                embeddingModel,
-              )
-              embeddingProgress.inserted += batchSize
-            }
-          },
-          {
-            numOfAttempts: 5,
-            startingDelay: 1000,
-            timeMultiple: 1.5,
-            jitter: 'full',
-            retry: (error) => {
-              console.error(error)
-              const isRateLimitError =
-                error.status === 429 &&
-                error.message.toLowerCase().includes('rate limit')
-              return !!isRateLimitError // retry only for rate limit errors
+        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,
+              })
+
+              // Insert vectors in batches
+              if (
+                embeddingChunks.length >=
+                  embeddingProgress.inserted + batchSize ||
+                embeddingChunks.length === contentChunks.length
+              ) {
+                await this.repository.insertVectors(
+                  embeddingChunks.slice(
+                    embeddingProgress.inserted,
+                    embeddingProgress.inserted + batchSize,
+                  ),
+                  embeddingModel,
+                )
+                embeddingProgress.inserted += batchSize
+              }
             },
-          },
-        )
+            {
+              numOfAttempts: 5,
+              startingDelay: 1000,
+              timeMultiple: 1.5,
+              jitter: 'full',
+              retry: (error) => {
+                console.error(error)
+                const isRateLimitError =
+                  error.status === 429 &&
+                  error.message.toLowerCase().includes('rate limit')
+                return !!isRateLimitError // retry only for rate limit errors
+              },
+            },
+          )
+        } catch (error) {
+          abortController.abort()
+          throw error
+        }
       }),
     )
 
     try {
       await Promise.all(tasks)
     } catch (error) {
-      console.error('Error embedding chunks:', error)
-      throw error
+      if (
+        error instanceof LLMAPIKeyNotSetException ||
+        error instanceof LLMAPIKeyInvalidException ||
+        error instanceof LLMBaseUrlNotSetException
+      ) {
+        openSettingsModalWithError(this.app, (error as Error).message)
+      } else {
+        console.error('Error embedding chunks:', error)
+        throw error
+      }
     } finally {
       await this.repository.save()
     }