mirror of
https://github.com/zadam/trilium.git
synced 2025-11-08 22:35:50 +01:00
add agentic thinking to chat
This commit is contained in:
perf3ct
@@ -77,6 +77,15 @@ Example: ["exact topic mentioned", "related concept 1", "related concept 2"]`;
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throw new Error(`No embedding provider available. Could not initialize context service.`);
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}
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// Initialize agent tools to ensure they're ready
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try {
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await aiServiceManager.getInstance().initializeAgentTools();
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log.info("Agent tools initialized for use with TriliumContextService");
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} catch (toolError) {
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log.error(`Error initializing agent tools: ${toolError}`);
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// Continue even if agent tools fail to initialize
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}
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this.initialized = true;
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log.info(`Trilium context service initialized with provider: ${this.provider.name}`);
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} catch (error: unknown) {
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@@ -549,17 +558,16 @@ Example: ["exact topic mentioned", "related concept 1", "related concept 2"]`;
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}
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/**
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* Process a user query with the Trilium-specific approach:
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* 1. Generate search queries from the original question
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* 2. Find relevant notes using those queries
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* 3. Build a context string from the relevant notes
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*
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* @param userQuestion - The user's original question
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* @param llmService - The LLM service to use
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* @param contextNoteId - Optional note ID to restrict search to
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* @returns Object with context and notes
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* Process a user query to find relevant context in Trilium notes
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*/
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async processQuery(userQuestion: string, llmService: any, contextNoteId: string | null = null) {
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async processQuery(
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userQuestion: string,
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llmService: any,
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contextNoteId: string | null = null,
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showThinking: boolean = false
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) {
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log.info(`Processing query with: question="${userQuestion.substring(0, 50)}...", noteId=${contextNoteId}, showThinking=${showThinking}`);
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if (!this.initialized) {
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try {
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await this.initialize();
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@@ -602,8 +610,28 @@ Example: ["exact topic mentioned", "related concept 1", "related concept 2"]`;
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// Step 3: Build context from the notes
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const context = await this.buildContextFromNotes(relevantNotes, userQuestion);
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// Step 4: Add agent tools context with thinking process if requested
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let enhancedContext = context;
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try {
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// Get agent tools context using either the specific note or the most relevant notes
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const agentContext = await this.getAgentToolsContext(
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contextNoteId || (relevantNotes[0]?.noteId || ""),
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userQuestion,
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showThinking,
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relevantNotes // Pass all relevant notes for context
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);
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if (agentContext) {
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enhancedContext = `${context}\n\n${agentContext}`;
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log.info(`Added agent tools context (${agentContext.length} characters)`);
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}
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} catch (error) {
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log.error(`Error getting agent tools context: ${error}`);
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// Continue with just the basic context
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}
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return {
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context,
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context: enhancedContext,
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notes: relevantNotes,
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queries: searchQueries
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};
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@@ -628,31 +656,57 @@ Example: ["exact topic mentioned", "related concept 1", "related concept 2"]`;
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* 3. Query decomposition planning
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* 4. Contextual thinking visualization
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*
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* @param noteId The current note being viewed
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* @param noteId The current note being viewed (or most relevant note)
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* @param query The user's query
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* @param showThinking Whether to include the agent's thinking process
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* @param relevantNotes Optional array of relevant notes from vector search
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* @returns Enhanced context string
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*/
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async getAgentToolsContext(noteId: string, query: string, showThinking: boolean = false): Promise<string> {
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async getAgentToolsContext(
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noteId: string,
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query: string,
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showThinking: boolean = false,
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relevantNotes: Array<any> = []
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): Promise<string> {
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log.info(`Getting agent tools context: noteId=${noteId}, query="${query.substring(0, 50)}...", showThinking=${showThinking}, relevantNotesCount=${relevantNotes.length}`);
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try {
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const agentTools = aiServiceManager.getAgentTools();
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let context = "";
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// 1. Get vector search results related to the query
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try {
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const vectorSearchTool = agentTools.getVectorSearchTool();
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const searchResults = await vectorSearchTool.searchNotes(query, {
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parentNoteId: noteId,
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maxResults: 5
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});
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if (searchResults.length > 0) {
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// If we already have relevant notes from vector search, use those
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if (relevantNotes && relevantNotes.length > 0) {
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log.info(`Using ${relevantNotes.length} provided relevant notes instead of running vector search again`);
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context += "## Related Information\n\n";
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for (const result of searchResults) {
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for (const result of relevantNotes.slice(0, 5)) {
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context += `### ${result.title}\n`;
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context += `${result.contentPreview}\n\n`;
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// Use the content if available, otherwise get a preview
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const contentPreview = result.content
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? this.sanitizeNoteContent(result.content).substring(0, 300) + "..."
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: result.contentPreview || "[No preview available]";
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context += `${contentPreview}\n\n`;
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}
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context += "\n";
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} else {
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// Run vector search if we don't have relevant notes
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const vectorSearchTool = agentTools.getVectorSearchTool();
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const searchResults = await vectorSearchTool.searchNotes(query, {
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parentNoteId: noteId,
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maxResults: 5
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});
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if (searchResults.length > 0) {
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context += "## Related Information\n\n";
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for (const result of searchResults) {
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context += `### ${result.title}\n`;
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context += `${result.contentPreview}\n\n`;
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}
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context += "\n";
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}
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}
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} catch (error: any) {
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log.error(`Error getting vector search context: ${error.message}`);
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@@ -694,55 +748,114 @@ Example: ["exact topic mentioned", "related concept 1", "related concept 2"]`;
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// 4. Show thinking process if enabled
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if (showThinking) {
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log.info("Showing thinking process - creating visual reasoning steps");
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try {
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const thinkingTool = agentTools.getContextualThinkingTool();
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const thinkingId = thinkingTool.startThinking(query);
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log.info(`Started thinking process with ID: ${thinkingId}`);
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// Add a thinking step to demonstrate the feature
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// In a real implementation, the LLM would add these steps
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// Add initial thinking steps
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thinkingTool.addThinkingStep(
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"Analyzing the query to understand what information is needed",
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"Analyzing the user's query to understand the information needs",
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"observation",
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{ confidence: 1.0 }
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);
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// Add sample thinking for the context
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// Add query exploration steps
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const parentId = thinkingTool.addThinkingStep(
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"Looking for related notes in the knowledge base",
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"Exploring knowledge base to find relevant information",
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"hypothesis",
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{ confidence: 0.9 }
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);
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if (parentId) {
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// Use the VectorSearchTool to find relevant notes
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const vectorSearchTool = aiServiceManager.getVectorSearchTool();
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const searchResults = await vectorSearchTool.searchNotes(query, {
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parentNoteId: parentId,
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maxResults: 5
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});
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// Add information about relevant notes if available
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if (relevantNotes && relevantNotes.length > 0) {
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const noteTitles = relevantNotes.slice(0, 5).map(n => n.title).join(", ");
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thinkingTool.addThinkingStep(
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`Found ${relevantNotes.length} potentially relevant notes through semantic search, including: ${noteTitles}`,
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"evidence",
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{ confidence: 0.85, parentId: parentId || undefined }
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);
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}
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if (searchResults.length > 0) {
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context += "## Related Information\n\n";
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for (const result of searchResults) {
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context += `### ${result.title}\n`;
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context += `${result.contentPreview}\n\n`;
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// Add step about note hierarchy if a specific note is being viewed
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if (noteId && noteId !== "") {
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try {
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const navigatorTool = agentTools.getNoteNavigatorTool();
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// Get parent notes since we don't have getNoteHierarchyInfo
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const parents = navigatorTool.getParentNotes(noteId);
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if (parents && parents.length > 0) {
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const parentInfo = parents.map(p => p.title).join(" > ");
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thinkingTool.addThinkingStep(
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`Identified note hierarchy context: ${parentInfo}`,
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"evidence",
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{ confidence: 0.9, parentId: parentId || undefined }
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);
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}
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context += "\n";
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} catch (error) {
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log.error(`Error getting note hierarchy: ${error}`);
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}
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}
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// Add query decomposition if it's a complex query
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try {
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const decompositionTool = agentTools.getQueryDecompositionTool();
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const complexity = decompositionTool.assessQueryComplexity(query);
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if (complexity > 4) {
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thinkingTool.addThinkingStep(
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`This is a ${complexity > 7 ? "very complex" : "moderately complex"} query (complexity: ${complexity}/10)`,
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"observation",
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{ confidence: 0.8 }
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);
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const decomposed = decompositionTool.decomposeQuery(query);
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if (decomposed.subQueries.length > 1) {
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const decompId = thinkingTool.addThinkingStep(
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"Breaking down query into sub-questions to address systematically",
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"hypothesis",
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{ confidence: 0.85 }
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);
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for (const sq of decomposed.subQueries) {
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thinkingTool.addThinkingStep(
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`Subquery: ${sq.text} - ${sq.reason}`,
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"evidence",
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{ confidence: 0.8, parentId: decompId || undefined }
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);
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}
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}
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} else {
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thinkingTool.addThinkingStep(
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`This is a straightforward query (complexity: ${complexity}/10) that can be addressed directly`,
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"observation",
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{ confidence: 0.9 }
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);
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}
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} catch (error) {
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log.error(`Error in query decomposition: ${error}`);
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}
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// Add final conclusions
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thinkingTool.addThinkingStep(
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"The most relevant information appears to be in the current note and its semantic neighborhood",
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"Ready to formulate response based on available information and query understanding",
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"conclusion",
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{ confidence: 0.85 }
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{ confidence: 0.95 }
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);
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// Complete the thinking and add it to context
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// Complete the thinking process and add the visualization to context
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thinkingTool.completeThinking(thinkingId);
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context += "## Thinking Process\n\n";
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context += thinkingTool.getThinkingSummary(thinkingId) + "\n\n";
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const visualization = thinkingTool.visualizeThinking(thinkingId);
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if (visualization) {
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context += "## Reasoning Process\n\n";
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context += visualization + "\n\n";
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log.info(`Added thinking visualization to context (${visualization.length} characters)`);
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}
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} catch (error: any) {
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log.error(`Error generating thinking process: ${error.message}`);
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log.error(`Error creating thinking visualization: ${error.message}`);
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}
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}
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