use web worker to run pglite

src/core/llm/gemini.ts

@@ -60,6 +60,7 @@ export class GeminiProvider implements BaseLLMProvider {
 				: undefined
 
 		try {
+			console.log(request)
 			const model = this.client.getGenerativeModel({
 				model: request.model,
 				generationConfig: {

src/core/prompts/sections/capabilities.ts

@@ -28,12 +28,9 @@ function getDeepResearchCapabilitiesSection(): string {
 CAPABILITIES
 
 - You have access to tools that let you search the web using internet search engines like Google to find relevant information on current events, facts, data, and other online content.
-- Using search_web, you can simulate a human research process: first searching with relevant keywords to obtain initial results (containing URLs, titles, and content snippets).
-- You should evaluate the relevance and reliability of each search result based on its title and content snippet, then select the most relevant URLs for deeper investigation.
-- Use fetch_urls_content to retrieve complete webpage content from selected URLs to gain detailed information.
-- You can conduct multiple rounds of searches and content retrieval (maximum 3 rounds), optimizing your search keywords in each round based on previously gathered information, just as a human would perform deep research.
-- Synthesize all collected information to answer the user's questions comprehensively, accurately, and in a well-structured manner, citing information sources when appropriate.
-`
+- Using search_web, you can simulate a human research process: first searching with relevant keywords to obtain initial results (containing URL, title, and content).
+- Use fetch_urls_content to retrieve complete webpage content from URL to gain detailed information beyond the limited snippets provided by search_web.
+- Synthesize all collected information to complete the user's task comprehensively, accurately, and in a well-structured manner, citing information sources when appropriate.`
 }
 
 export function getCapabilitiesSection(
@@ -45,4 +42,4 @@ export function getCapabilitiesSection(
 		return getDeepResearchCapabilitiesSection();
 	}
 	return getObsidianCapabilitiesSection(cwd, searchWebTool);
-}
+}

src/core/prompts/tools/search-web.ts

@@ -16,15 +16,17 @@ Usage:
 <query>Your search query here</query>
 </search_web>
 
-Examples: 
+Examples1: 
 <search_web>
 <query>capital of France population statistics 2023</query>
 </search_web>
 
+Examples2:
 <search_web>
 <query>"renewable energy" growth statistics Europe</query>
 </search_web>
 
+Examples3:
 <search_web>
 <query>react vs angular vs vue.js comparison</query>
 </search_web>`

src/core/rag/rag-engine.ts

@@ -1,4 +1,4 @@
-import { App } from 'obsidian'
+import { App, TFile } from 'obsidian'
 
 import { QueryProgressState } from '../../components/chat-view/QueryProgress'
 import { DBManager } from '../../database/database-manager'
@@ -13,7 +13,8 @@ export class RAGEngine {
   private app: App
   private settings: InfioSettings
   private vectorManager: VectorManager
-  private embeddingModel: EmbeddingModel | null = null
+	private embeddingModel: EmbeddingModel | null = null
+	private initialized = false
 
   constructor(
     app: App,
@@ -23,7 +24,7 @@ export class RAGEngine {
     this.app = app
     this.settings = settings
     this.vectorManager = dbManager.getVectorManager()
-    this.embeddingModel = getEmbeddingModel(settings)
+		this.embeddingModel = getEmbeddingModel(settings)
   }
 
   setSettings(settings: InfioSettings) {
@@ -34,16 +35,14 @@ export class RAGEngine {
   // TODO: Implement automatic vault re-indexing when settings are changed.
   // Currently, users must manually re-index the vault.
   async updateVaultIndex(
-    options: { reindexAll: boolean } = {
-      reindexAll: false,
-    },
+    options: { reindexAll: boolean },
     onQueryProgressChange?: (queryProgress: QueryProgressState) => void,
-  ): Promise<void> {
-    if (!this.embeddingModel) {
-      throw new Error('Embedding model is not set')
+	): Promise<void> {
+		if (!this.embeddingModel) {
+			throw new Error('Embedding model is not set')
     }
     await this.vectorManager.updateVaultIndex(
-      this.embeddingModel,
+			this.embeddingModel,
       {
         chunkSize: this.settings.ragOptions.chunkSize,
         excludePatterns: this.settings.ragOptions.excludePatterns,
@@ -57,7 +56,23 @@ export class RAGEngine {
         })
       },
     )
-  }
+		this.initialized = true
+	}
+
+  async updateFileIndex(file: TFile) {
+    await this.vectorManager.UpdateFileVectorIndex(
+      this.embeddingModel,
+      this.settings.ragOptions.chunkSize,
+      file,
+    )
+	}
+	
+	async deleteFileIndex(file: TFile) {
+		await this.vectorManager.DeleteFileVectorIndex(
+			this.embeddingModel,
+			file,
+		)
+	}
 
   async processQuery({
     query,
@@ -78,13 +93,19 @@ export class RAGEngine {
     if (!this.embeddingModel) {
       throw new Error('Embedding model is not set')
     }
-    // TODO: Decide the vault index update strategy.
-    // Current approach: Update on every query.
-    await this.updateVaultIndex({ reindexAll: false }, onQueryProgressChange)
+
+		if (!this.initialized) {
+      await this.updateVaultIndex({ reindexAll: false }, onQueryProgressChange)
+    }
     const queryEmbedding = await this.getQueryEmbedding(query)
     onQueryProgressChange?.({
       type: 'querying',
-    })
+		})
+		console.log('query, ', {
+			minSimilarity: this.settings.ragOptions.minSimilarity,
+			limit: this.settings.ragOptions.limit,
+			scope,
+		})
     const queryResult = await this.vectorManager.performSimilaritySearch(
       queryEmbedding,
       this.embeddingModel,
@@ -93,7 +114,8 @@ export class RAGEngine {
         limit: this.settings.ragOptions.limit,
         scope,
       },
-    )
+		)
+		console.log('queryResult', queryResult)
     onQueryProgressChange?.({
       type: 'querying-done',
       queryResult,