mirror of
https://github.com/TriliumNext/Notes.git
synced 2025-09-19 02:10:04 +08:00
try a context approach
This commit is contained in:
perf3ct
parent
adaac46fbf
commit
cf0e9242a0
@ -24,6 +24,7 @@ export default class LlmChatPanel extends BasicWidget {
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private chatContainer!: HTMLElement;
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private loadingIndicator!: HTMLElement;
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private sourcesList!: HTMLElement;
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private useAdvancedContextCheckbox!: HTMLInputElement;
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private sessionId: string | null = null;
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private currentNoteId: string | null = null;
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@ -45,7 +46,20 @@ export default class LlmChatPanel extends BasicWidget {
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<div class="sources-list"></div>
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</div>
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<form class="note-context-chat-form d-flex border-top p-2">
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<form class="note-context-chat-form d-flex flex-column border-top p-2">
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<div class="d-flex mb-2 align-items-center">
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<div class="form-check form-switch">
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<input class="form-check-input use-advanced-context-checkbox" type="checkbox" id="useAdvancedContext" checked>
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<label class="form-check-label" for="useAdvancedContext">
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${t('ai.use_advanced_context')}
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</label>
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</div>
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<div class="ms-2 small text-muted">
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<i class="bx bx-info-circle"></i>
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<span>${t('ai.advanced_context_helps')}</span>
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</div>
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</div>
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<div class="d-flex">
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<textarea
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class="form-control note-context-chat-input"
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placeholder="${t('ai.enter_message')}"
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@ -54,6 +68,7 @@ export default class LlmChatPanel extends BasicWidget {
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<button type="submit" class="btn btn-primary note-context-chat-send-button ms-2">
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<i class="bx bx-send"></i>
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</button>
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</div>
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</form>
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</div>
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`);
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@ -66,6 +81,7 @@ export default class LlmChatPanel extends BasicWidget {
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this.chatContainer = element.querySelector('.note-context-chat-container') as HTMLElement;
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this.loadingIndicator = element.querySelector('.loading-indicator') as HTMLElement;
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this.sourcesList = element.querySelector('.sources-list') as HTMLElement;
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this.useAdvancedContextCheckbox = element.querySelector('.use-advanced-context-checkbox') as HTMLInputElement;
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this.initializeEventListeners();
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@ -109,47 +125,67 @@ export default class LlmChatPanel extends BasicWidget {
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return;
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}
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this.showLoadingIndicator();
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try {
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// Add user message to chat
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this.addMessageToChat('user', content);
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this.noteContextChatInput.value = '';
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this.showLoadingIndicator();
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this.hideSources();
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// Get AI settings
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const useRAG = true; // Always use RAG for this widget
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try {
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const useAdvancedContext = this.useAdvancedContextCheckbox.checked;
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// Send message to server
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const response = await server.post<ChatResponse>('llm/sessions/' + this.sessionId + '/messages', {
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sessionId: this.sessionId,
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content: content,
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options: {
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useRAG: useRAG
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// Setup streaming
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const source = new EventSource(`./api/llm/messages?sessionId=${this.sessionId}&format=stream`);
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let assistantResponse = '';
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// Handle streaming response
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source.onmessage = (event) => {
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if (event.data === '[DONE]') {
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// Stream completed
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source.close();
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this.hideLoadingIndicator();
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return;
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}
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try {
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const data = JSON.parse(event.data);
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if (data.content) {
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assistantResponse += data.content;
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// Update the UI with the accumulated response
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const assistantElement = this.noteContextChatMessages.querySelector('.assistant-message:last-child .message-content');
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if (assistantElement) {
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assistantElement.innerHTML = this.formatMarkdown(assistantResponse);
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} else {
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this.addMessageToChat('assistant', assistantResponse);
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}
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// Scroll to the bottom
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this.chatContainer.scrollTop = this.chatContainer.scrollHeight;
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}
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} catch (e) {
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console.error('Error parsing SSE message:', e);
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}
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};
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source.onerror = () => {
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source.close();
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this.hideLoadingIndicator();
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toastService.showError('Error connecting to the LLM service. Please try again.');
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};
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// Send the actual message
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const response = await server.post<any>('llm/messages', {
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sessionId: this.sessionId,
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content,
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contextNoteId: this.currentNoteId,
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useAdvancedContext
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});
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// Get the assistant's message (last one)
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if (response?.messages?.length) {
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const messages = response.messages;
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const lastMessage = messages[messages.length - 1];
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if (lastMessage && lastMessage.role === 'assistant') {
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this.addMessageToChat('assistant', lastMessage.content);
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}
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}
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// Display sources if available
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if (response?.sources?.length) {
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// Handle sources if returned in non-streaming response
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if (response && response.sources && response.sources.length > 0) {
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this.showSources(response.sources);
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} else {
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this.hideSources();
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}
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} catch (error) {
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console.error('Failed to send message:', error);
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toastService.showError('Failed to send message to AI');
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} finally {
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this.hideLoadingIndicator();
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toastService.showError('Error sending message: ' + (error as Error).message);
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}
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}
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@ -243,4 +279,17 @@ export default class LlmChatPanel extends BasicWidget {
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}
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});
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}
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/**
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* Format markdown content for display
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*/
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private formatMarkdown(content: string): string {
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// Simple markdown formatting - could be replaced with a proper markdown library
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return content
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.replace(/\*\*(.*?)\*\*/g, '<strong>$1</strong>')
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.replace(/\*(.*?)\*/g, '<em>$1</em>')
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.replace(/`(.*?)`/g, '<code>$1</code>')
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.replace(/\n/g, '<br>')
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.replace(/```(.*?)```/gs, '<pre><code>$1</code></pre>');
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}
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}
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@ -1755,5 +1755,11 @@
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"content_language": {
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"title": "Content languages",
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"description": "Select one or more languages that should appear in the language selection in the Basic Properties section of a read-only or editable text note. This will allow features such as spell-checking or right-to-left support."
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},
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"ai": {
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"sources": "Sources",
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"enter_message": "Enter your message...",
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"use_advanced_context": "Use Advanced Context",
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"advanced_context_helps": "Helps with large knowledge bases and limited context windows"
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}
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}
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@ -9,6 +9,8 @@ import providerManager from "../../services/llm/embeddings/providers.js";
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import type { Message, ChatCompletionOptions } from "../../services/llm/ai_interface.js";
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// Import this way to prevent immediate instantiation
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import * as aiServiceManagerModule from "../../services/llm/ai_service_manager.js";
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import triliumContextService from "../../services/llm/trilium_context_service.js";
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import sql from "../../services/sql.js";
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// Define basic interfaces
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interface ChatMessage {
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@ -290,132 +292,126 @@ async function deleteSession(req: Request, res: Response) {
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}
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/**
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* Find relevant notes using vector embeddings
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* Find relevant notes based on search query
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*/
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async function findRelevantNotes(query: string, contextNoteId: string | null = null, limit = 5): Promise<NoteSource[]> {
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async function findRelevantNotes(content: string, contextNoteId: string | null = null, limit = 5): Promise<NoteSource[]> {
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try {
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// Only proceed if database is initialized
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// If database is not initialized, we can't do this
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if (!isDatabaseInitialized()) {
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log.info('Database not initialized, skipping vector search');
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return [{
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noteId: "root",
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title: "Database not initialized yet",
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content: "Please wait for database initialization to complete."
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}];
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return [];
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}
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// Get the default embedding provider
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let providerId;
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try {
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// @ts-ignore - embeddingsDefaultProvider exists but might not be in the TypeScript definitions
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providerId = await options.getOption('embeddingsDefaultProvider') || 'openai';
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} catch (error) {
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log.info('Could not get default embedding provider, using mock data');
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return [{
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noteId: "root",
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title: "Embeddings not configured",
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content: "Embedding provider not available"
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}];
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// Check if embeddings are available
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const enabledProviders = await providerManager.getEnabledEmbeddingProviders();
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if (enabledProviders.length === 0) {
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log.info("No embedding providers available, can't find relevant notes");
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return [];
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}
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const provider = providerManager.getEmbeddingProvider(providerId);
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if (!provider) {
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log.info(`Embedding provider ${providerId} not found, using mock data`);
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return [{
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noteId: "root",
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title: "Embeddings not available",
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content: "No embedding provider available"
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}];
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// If content is too short, don't bother
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if (content.length < 3) {
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return [];
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}
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// Generate embedding for the query
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const embedding = await provider.generateEmbeddings(query);
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// Get the embedding for the query
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const provider = enabledProviders[0];
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const embedding = await provider.generateEmbeddings(content);
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// Find similar notes
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const modelId = 'default'; // Use default model for the provider
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const similarNotes = await vectorStore.findSimilarNotes(
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embedding, providerId, modelId, limit, 0.6 // Lower threshold to find more results
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let results;
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if (contextNoteId) {
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// For branch context, get notes specifically from that branch
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// TODO: This is a simplified implementation - we need to
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// properly get all notes in the subtree starting from contextNoteId
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// For now, just get direct children of the context note
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const contextNote = becca.notes[contextNoteId];
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if (!contextNote) {
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return [];
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}
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const childBranches = await sql.getRows(`
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SELECT branches.* FROM branches
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WHERE branches.parentNoteId = ?
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AND branches.isDeleted = 0
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`, [contextNoteId]);
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const childNoteIds = childBranches.map((branch: any) => branch.noteId);
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// Include the context note itself
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childNoteIds.push(contextNoteId);
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// Find similar notes in this context
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results = [];
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for (const noteId of childNoteIds) {
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const noteEmbedding = await vectorStore.getEmbeddingForNote(
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noteId,
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provider.name,
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provider.getConfig().model
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);
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// If a context note was provided, check if we should include its children
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if (contextNoteId) {
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const contextNote = becca.getNote(contextNoteId);
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if (contextNote) {
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const childNotes = contextNote.getChildNotes();
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if (childNotes.length > 0) {
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// Add relevant children that weren't already included
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const childIds = new Set(childNotes.map(note => note.noteId));
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const existingIds = new Set(similarNotes.map(note => note.noteId));
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if (noteEmbedding) {
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const similarity = vectorStore.cosineSimilarity(
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embedding,
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noteEmbedding.embedding
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);
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// Find children that aren't already in the similar notes
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const missingChildIds = Array.from(childIds).filter(id => !existingIds.has(id));
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// Add up to 3 children that weren't already included
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for (const noteId of missingChildIds.slice(0, 3)) {
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similarNotes.push({
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if (similarity > 0.65) {
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results.push({
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noteId,
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similarity: 0.75 // Fixed similarity score for context children
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similarity
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});
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}
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}
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}
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// Sort by similarity
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results.sort((a, b) => b.similarity - a.similarity);
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results = results.slice(0, limit);
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} else {
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// General search across all notes
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results = await vectorStore.findSimilarNotes(
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embedding,
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provider.name,
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provider.getConfig().model,
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limit
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);
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}
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// Get note content for context
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return await Promise.all(similarNotes.map(async ({ noteId, similarity }) => {
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const note = becca.getNote(noteId);
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if (!note) {
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return {
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noteId,
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title: "Unknown Note",
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similarity
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};
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// Format the results
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const sources: NoteSource[] = [];
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for (const result of results) {
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const note = becca.notes[result.noteId];
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if (!note) continue;
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let noteContent: string | undefined = undefined;
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if (note.type === 'text') {
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const content = note.getContent();
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// Handle both string and Buffer types
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noteContent = typeof content === 'string' ? content :
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content instanceof Buffer ? content.toString('utf8') : undefined;
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}
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// Get note content
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let content = '';
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try {
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// @ts-ignore - Content can be string or Buffer
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const noteContent = await note.getContent();
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content = typeof noteContent === 'string' ? noteContent : noteContent.toString('utf8');
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// Truncate content if it's too long (for performance)
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if (content.length > 2000) {
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content = content.substring(0, 2000) + "...";
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}
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} catch (e) {
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log.error(`Error getting content for note ${noteId}: ${e}`);
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}
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// Get a branch ID for navigation
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let branchId;
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try {
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const branches = note.getBranches();
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if (branches.length > 0) {
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branchId = branches[0].branchId;
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}
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} catch (e) {
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log.error(`Error getting branch for note ${noteId}: ${e}`);
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}
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return {
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noteId,
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sources.push({
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noteId: result.noteId,
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title: note.title,
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content,
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similarity,
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branchId
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};
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}));
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} catch (error) {
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log.error(`Error finding relevant notes: ${error}`);
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// Return empty array on error
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content: noteContent,
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similarity: result.similarity,
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branchId: note.getBranches()[0]?.branchId
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});
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}
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return sources;
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} catch (error: any) {
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log.error(`Error finding relevant notes: ${error.message}`);
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return [];
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}
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}
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/**
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* Build a context string from relevant notes
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* Build context from notes
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*/
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function buildContextFromNotes(sources: NoteSource[], query: string): string {
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console.log("Building context from notes with query:", query);
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@ -449,265 +445,237 @@ Now, based on the above notes, please answer: ${query}`;
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}
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/**
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* Send a message to an LLM chat session and get a response
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* Send a message to the AI
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*/
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async function sendMessage(req: Request, res: Response) {
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try {
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const { sessionId, content, temperature, maxTokens, provider, model } = req.body;
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console.log("Received message request:", {
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sessionId,
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contentLength: content ? content.length : 0,
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contentPreview: content ? content.substring(0, 50) + (content.length > 50 ? '...' : '') : 'undefined',
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temperature,
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maxTokens,
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provider,
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model
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});
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if (!sessionId) {
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throw new Error('Session ID is required');
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}
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// Extract the content from the request body
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const { content, sessionId, useAdvancedContext = false } = req.body || {};
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// Validate the content
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if (!content || typeof content !== 'string' || content.trim().length === 0) {
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throw new Error('Content cannot be empty');
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}
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// Check if streaming is requested
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const wantsStream = (req.headers as any)['accept']?.includes('text/event-stream');
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// Get or create the session
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let session: ChatSession;
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// If client wants streaming, set up SSE response
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if (wantsStream) {
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if (sessionId && sessions.has(sessionId)) {
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session = sessions.get(sessionId)!;
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session.lastActive = new Date();
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} else {
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const result = await createSession(req, res);
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if (!result?.id) {
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throw new Error('Failed to create a new session');
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}
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session = sessions.get(result.id)!;
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}
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// Check if AI services are available
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if (!safelyUseAIManager()) {
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throw new Error('AI services are not available');
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}
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// Get the AI service manager
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const aiServiceManager = aiServiceManagerModule.default.getInstance();
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// Get the default service - just use the first available one
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const availableProviders = aiServiceManager.getAvailableProviders();
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let service = null;
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if (availableProviders.length > 0) {
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// Use the first available provider
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const providerName = availableProviders[0];
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// We know the manager has a 'services' property from our code inspection,
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// but TypeScript doesn't know that from the interface.
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// This is a workaround to access it
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service = (aiServiceManager as any).services[providerName];
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}
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if (!service) {
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throw new Error('No AI service is available');
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}
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// Create user message
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const userMessage: Message = {
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role: 'user',
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content
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};
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// Add message to session
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session.messages.push({
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role: 'user',
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content,
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timestamp: new Date()
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});
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// Log a preview of the message
|
||||
log.info(`Processing LLM message: "${content.substring(0, 50)}${content.length > 50 ? '...' : ''}"`);
|
||||
|
||||
// Information to return to the client
|
||||
let aiResponse = '';
|
||||
let sourceNotes: NoteSource[] = [];
|
||||
|
||||
// If Advanced Context is enabled, we use the improved method
|
||||
if (useAdvancedContext) {
|
||||
// Use the Trilium-specific approach
|
||||
const contextNoteId = session.noteContext || null;
|
||||
const results = await triliumContextService.processQuery(content, service, contextNoteId);
|
||||
|
||||
// Get the generated context
|
||||
const context = results.context;
|
||||
sourceNotes = results.notes;
|
||||
|
||||
// Add system message with the context
|
||||
const contextMessage: Message = {
|
||||
role: 'system',
|
||||
content: context
|
||||
};
|
||||
|
||||
// Format all messages for the AI
|
||||
const aiMessages: Message[] = [
|
||||
contextMessage,
|
||||
...session.messages.slice(-10).map(msg => ({
|
||||
role: msg.role,
|
||||
content: msg.content
|
||||
}))
|
||||
];
|
||||
|
||||
// Configure chat options from session metadata
|
||||
const chatOptions: ChatCompletionOptions = {
|
||||
temperature: session.metadata.temperature || 0.7,
|
||||
maxTokens: session.metadata.maxTokens,
|
||||
model: session.metadata.model
|
||||
// 'provider' property has been removed as it's not in the ChatCompletionOptions type
|
||||
};
|
||||
|
||||
// Get streaming response if requested
|
||||
const acceptHeader = req.get('Accept');
|
||||
if (acceptHeader && acceptHeader.includes('text/event-stream')) {
|
||||
res.setHeader('Content-Type', 'text/event-stream');
|
||||
res.setHeader('Cache-Control', 'no-cache');
|
||||
res.setHeader('Connection', 'keep-alive');
|
||||
|
||||
// Get chat session
|
||||
let session = sessions.get(sessionId);
|
||||
if (!session) {
|
||||
const newSession = await createSession(req, res);
|
||||
if (!newSession) {
|
||||
throw new Error('Failed to create session');
|
||||
}
|
||||
// Add required properties to match ChatSession interface
|
||||
session = {
|
||||
...newSession,
|
||||
messages: [],
|
||||
lastActive: new Date(),
|
||||
metadata: {}
|
||||
};
|
||||
sessions.set(sessionId, session);
|
||||
let messageContent = '';
|
||||
|
||||
// Stream the response
|
||||
await service.sendChatCompletion(
|
||||
aiMessages,
|
||||
chatOptions,
|
||||
(chunk: string) => {
|
||||
messageContent += chunk;
|
||||
res.write(`data: ${JSON.stringify({ content: chunk })}\n\n`);
|
||||
}
|
||||
);
|
||||
|
||||
// Add user message to session
|
||||
const userMessage: ChatMessage = {
|
||||
role: 'user',
|
||||
content: content,
|
||||
timestamp: new Date()
|
||||
};
|
||||
console.log("Created user message:", {
|
||||
role: userMessage.role,
|
||||
contentLength: userMessage.content?.length || 0,
|
||||
contentPreview: userMessage.content?.substring(0, 50) + (userMessage.content?.length > 50 ? '...' : '') || 'undefined'
|
||||
});
|
||||
session.messages.push(userMessage);
|
||||
|
||||
// Get context for query
|
||||
const sources = await findRelevantNotes(content, session.noteContext || null);
|
||||
|
||||
// Format messages for AI with proper type casting
|
||||
const aiMessages: Message[] = [
|
||||
{ role: 'system', content: 'You are a helpful assistant for Trilium Notes. When providing answers, use only the context provided in the notes. If the information is not in the notes, say so.' },
|
||||
{ role: 'user', content: buildContextFromNotes(sources, content) }
|
||||
];
|
||||
|
||||
// Ensure we're not sending empty content
|
||||
console.log("Final message content length:", aiMessages[1].content.length);
|
||||
console.log("Final message content preview:", aiMessages[1].content.substring(0, 100));
|
||||
|
||||
try {
|
||||
// Send initial SSE message with session info
|
||||
const sourcesForResponse = sources.map(({ noteId, title, similarity, branchId }) => ({
|
||||
noteId,
|
||||
title,
|
||||
similarity: similarity ? Math.round(similarity * 100) / 100 : undefined,
|
||||
branchId
|
||||
}));
|
||||
|
||||
res.write(`data: ${JSON.stringify({
|
||||
type: 'init',
|
||||
session: {
|
||||
id: sessionId,
|
||||
messages: session.messages.slice(0, -1), // Don't include the new message yet
|
||||
sources: sourcesForResponse
|
||||
}
|
||||
})}\n\n`);
|
||||
|
||||
// Get AI response with streaming enabled
|
||||
const aiResponse = await aiServiceManagerModule.default.generateChatCompletion(aiMessages, {
|
||||
temperature,
|
||||
maxTokens,
|
||||
model: provider ? `${provider}:${model}` : model,
|
||||
stream: true
|
||||
});
|
||||
|
||||
if (aiResponse.stream) {
|
||||
// Create an empty assistant message
|
||||
const assistantMessage: ChatMessage = {
|
||||
role: 'assistant',
|
||||
content: '',
|
||||
timestamp: new Date()
|
||||
};
|
||||
session.messages.push(assistantMessage);
|
||||
|
||||
// Stream the response chunks
|
||||
await aiResponse.stream(async (chunk) => {
|
||||
if (chunk.text) {
|
||||
// Update the message content
|
||||
assistantMessage.content += chunk.text;
|
||||
|
||||
// Send chunk to client
|
||||
res.write(`data: ${JSON.stringify({
|
||||
type: 'chunk',
|
||||
text: chunk.text,
|
||||
done: chunk.done
|
||||
})}\n\n`);
|
||||
}
|
||||
|
||||
if (chunk.done) {
|
||||
// Send final message with complete response
|
||||
res.write(`data: ${JSON.stringify({
|
||||
type: 'done',
|
||||
session: {
|
||||
id: sessionId,
|
||||
messages: session.messages,
|
||||
sources: sourcesForResponse
|
||||
}
|
||||
})}\n\n`);
|
||||
|
||||
// Close the stream
|
||||
res.write('data: [DONE]\n\n');
|
||||
res.end();
|
||||
}
|
||||
});
|
||||
|
||||
return; // Early return for streaming
|
||||
// Store the full response
|
||||
aiResponse = messageContent;
|
||||
} else {
|
||||
// Fallback for non-streaming response
|
||||
const assistantMessage: ChatMessage = {
|
||||
role: 'assistant',
|
||||
content: aiResponse.text,
|
||||
timestamp: new Date()
|
||||
// Non-streaming approach
|
||||
aiResponse = await service.sendChatCompletion(aiMessages, chatOptions);
|
||||
}
|
||||
} else {
|
||||
// Original approach - find relevant notes through direct embedding comparison
|
||||
const relevantNotes = await findRelevantNotes(
|
||||
content,
|
||||
session.noteContext || null,
|
||||
5
|
||||
);
|
||||
|
||||
sourceNotes = relevantNotes;
|
||||
|
||||
// Build context from relevant notes
|
||||
const context = buildContextFromNotes(relevantNotes, content);
|
||||
|
||||
// Add system message with the context
|
||||
const contextMessage: Message = {
|
||||
role: 'system',
|
||||
content: context
|
||||
};
|
||||
session.messages.push(assistantMessage);
|
||||
|
||||
// Send complete response
|
||||
res.write(`data: ${JSON.stringify({
|
||||
type: 'done',
|
||||
session: {
|
||||
id: sessionId,
|
||||
messages: session.messages,
|
||||
sources: sourcesForResponse
|
||||
}
|
||||
})}\n\n`);
|
||||
|
||||
res.end();
|
||||
return;
|
||||
}
|
||||
} catch (error: any) {
|
||||
// Send error in streaming format
|
||||
res.write(`data: ${JSON.stringify({
|
||||
type: 'error',
|
||||
error: `AI service error: ${error.message}`
|
||||
})}\n\n`);
|
||||
|
||||
res.end();
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
||||
// Non-streaming API continues with normal JSON response...
|
||||
|
||||
// Get chat session
|
||||
let session = sessions.get(sessionId);
|
||||
if (!session) {
|
||||
const newSession = await createSession(req, res);
|
||||
if (!newSession) {
|
||||
throw new Error('Failed to create session');
|
||||
}
|
||||
// Add required properties to match ChatSession interface
|
||||
session = {
|
||||
...newSession,
|
||||
messages: [],
|
||||
lastActive: new Date(),
|
||||
metadata: {}
|
||||
};
|
||||
sessions.set(sessionId, session);
|
||||
}
|
||||
|
||||
// Add user message to session
|
||||
const userMessage: ChatMessage = {
|
||||
role: 'user',
|
||||
content: content,
|
||||
timestamp: new Date()
|
||||
};
|
||||
console.log("Created user message:", {
|
||||
role: userMessage.role,
|
||||
contentLength: userMessage.content?.length || 0,
|
||||
contentPreview: userMessage.content?.substring(0, 50) + (userMessage.content?.length > 50 ? '...' : '') || 'undefined'
|
||||
});
|
||||
session.messages.push(userMessage);
|
||||
|
||||
// Get context for query
|
||||
const sources = await findRelevantNotes(content, session.noteContext || null);
|
||||
|
||||
// Format messages for AI with proper type casting
|
||||
// Format all messages for the AI
|
||||
const aiMessages: Message[] = [
|
||||
{ role: 'system', content: 'You are a helpful assistant for Trilium Notes. When providing answers, use only the context provided in the notes. If the information is not in the notes, say so.' },
|
||||
{ role: 'user', content: buildContextFromNotes(sources, content) }
|
||||
contextMessage,
|
||||
...session.messages.slice(-10).map(msg => ({
|
||||
role: msg.role,
|
||||
content: msg.content
|
||||
}))
|
||||
];
|
||||
|
||||
// Ensure we're not sending empty content
|
||||
console.log("Final message content length:", aiMessages[1].content.length);
|
||||
console.log("Final message content preview:", aiMessages[1].content.substring(0, 100));
|
||||
// Configure chat options from session metadata
|
||||
const chatOptions: ChatCompletionOptions = {
|
||||
temperature: session.metadata.temperature || 0.7,
|
||||
maxTokens: session.metadata.maxTokens,
|
||||
model: session.metadata.model
|
||||
// 'provider' property has been removed as it's not in the ChatCompletionOptions type
|
||||
};
|
||||
|
||||
try {
|
||||
// Get AI response using the safe accessor methods
|
||||
const aiResponse = await aiServiceManagerModule.default.generateChatCompletion(aiMessages, {
|
||||
temperature,
|
||||
maxTokens,
|
||||
model: provider ? `${provider}:${model}` : model,
|
||||
stream: false
|
||||
// Get streaming response if requested
|
||||
const acceptHeader = req.get('Accept');
|
||||
if (acceptHeader && acceptHeader.includes('text/event-stream')) {
|
||||
res.setHeader('Content-Type', 'text/event-stream');
|
||||
res.setHeader('Cache-Control', 'no-cache');
|
||||
res.setHeader('Connection', 'keep-alive');
|
||||
|
||||
let messageContent = '';
|
||||
|
||||
// Stream the response
|
||||
await service.sendChatCompletion(
|
||||
aiMessages,
|
||||
chatOptions,
|
||||
(chunk: string) => {
|
||||
messageContent += chunk;
|
||||
res.write(`data: ${JSON.stringify({ content: chunk })}\n\n`);
|
||||
}
|
||||
);
|
||||
|
||||
// Close the stream
|
||||
res.write('data: [DONE]\n\n');
|
||||
res.end();
|
||||
|
||||
// Store the full response
|
||||
aiResponse = messageContent;
|
||||
} else {
|
||||
// Non-streaming approach
|
||||
aiResponse = await service.sendChatCompletion(aiMessages, chatOptions);
|
||||
}
|
||||
}
|
||||
|
||||
// Only store the assistant's message if we're not streaming (otherwise we already did)
|
||||
const acceptHeader = req.get('Accept');
|
||||
if (!acceptHeader || !acceptHeader.includes('text/event-stream')) {
|
||||
// Store the assistant's response in the session
|
||||
session.messages.push({
|
||||
role: 'assistant',
|
||||
content: aiResponse,
|
||||
timestamp: new Date()
|
||||
});
|
||||
|
||||
// Add assistant message to session
|
||||
const assistantMessage: ChatMessage = {
|
||||
role: 'assistant',
|
||||
content: aiResponse.text,
|
||||
timestamp: new Date()
|
||||
};
|
||||
session.messages.push(assistantMessage);
|
||||
|
||||
// Format sources for the response (without content to reduce payload size)
|
||||
const sourcesForResponse = sources.map(({ noteId, title, similarity, branchId }) => ({
|
||||
noteId,
|
||||
title,
|
||||
similarity: similarity ? Math.round(similarity * 100) / 100 : undefined,
|
||||
branchId
|
||||
}));
|
||||
|
||||
// Return the response
|
||||
return {
|
||||
id: sessionId,
|
||||
messages: session.messages,
|
||||
sources: sourcesForResponse,
|
||||
provider: aiResponse.provider,
|
||||
model: aiResponse.model
|
||||
content: aiResponse,
|
||||
sources: sourceNotes.map(note => ({
|
||||
noteId: note.noteId,
|
||||
title: note.title,
|
||||
similarity: note.similarity,
|
||||
branchId: note.branchId
|
||||
}))
|
||||
};
|
||||
} catch (error: any) {
|
||||
log.error(`AI service error: ${error.message}`);
|
||||
throw new Error(`AI service error: ${error.message}`);
|
||||
} else {
|
||||
// For streaming responses, we've already sent the data
|
||||
// But we still need to add the message to the session
|
||||
session.messages.push({
|
||||
role: 'assistant',
|
||||
content: aiResponse,
|
||||
timestamp: new Date()
|
||||
});
|
||||
}
|
||||
} catch (error: any) {
|
||||
log.error(`Error sending message: ${error.message}`);
|
||||
throw error;
|
||||
log.error(`Error sending message to LLM: ${error.message}`);
|
||||
throw new Error(`Failed to send message: ${error.message}`);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
410
src/services/llm/trilium_context_service.ts
Normal file
410
src/services/llm/trilium_context_service.ts
Normal file
@ -0,0 +1,410 @@
|
||||
import becca from "../../becca/becca.js";
|
||||
import vectorStore from "./embeddings/vector_store.js";
|
||||
import providerManager from "./embeddings/providers.js";
|
||||
import options from "../options.js";
|
||||
import log from "../log.js";
|
||||
import type { Message } from "./ai_interface.js";
|
||||
import { cosineSimilarity } from "./embeddings/vector_store.js";
|
||||
|
||||
/**
|
||||
* TriliumContextService provides intelligent context management for working with large knowledge bases
|
||||
* through limited context window LLMs like Ollama.
|
||||
*
|
||||
* It creates a "meta-prompting" approach where the first LLM call is used
|
||||
* to determine what information might be needed to answer the query,
|
||||
* then only the relevant context is loaded, before making the final
|
||||
* response.
|
||||
*/
|
||||
class TriliumContextService {
|
||||
private initialized = false;
|
||||
private initPromise: Promise<void> | null = null;
|
||||
private provider: any = null;
|
||||
|
||||
// Cache for recently used context to avoid repeated embedding lookups
|
||||
private recentQueriesCache = new Map<string, {
|
||||
timestamp: number,
|
||||
relevantNotes: any[]
|
||||
}>();
|
||||
|
||||
// Configuration
|
||||
private cacheExpiryMs = 5 * 60 * 1000; // 5 minutes
|
||||
private metaPrompt = `You are an AI assistant that decides what information needs to be retrieved from a knowledge base to answer the user's question.
|
||||
Given the user's question, generate 3-5 specific search queries that would help find relevant information.
|
||||
Each query should be focused on a different aspect of the question.
|
||||
Format your answer as a JSON array of strings, with each string being a search query.
|
||||
Example: ["exact topic mentioned", "related concept 1", "related concept 2"]`;
|
||||
|
||||
constructor() {
|
||||
this.setupCacheCleanup();
|
||||
}
|
||||
|
||||
/**
|
||||
* Initialize the service
|
||||
*/
|
||||
async initialize() {
|
||||
if (this.initialized) return;
|
||||
|
||||
// Use a promise to prevent multiple simultaneous initializations
|
||||
if (this.initPromise) return this.initPromise;
|
||||
|
||||
this.initPromise = (async () => {
|
||||
try {
|
||||
const providerId = await options.getOption('embeddingsDefaultProvider') || 'ollama';
|
||||
this.provider = providerManager.getEmbeddingProvider(providerId);
|
||||
|
||||
if (!this.provider) {
|
||||
throw new Error(`Embedding provider ${providerId} not found`);
|
||||
}
|
||||
|
||||
this.initialized = true;
|
||||
log.info(`Trilium context service initialized with provider: ${providerId}`);
|
||||
} catch (error: unknown) {
|
||||
const errorMessage = error instanceof Error ? error.message : String(error);
|
||||
log.error(`Failed to initialize Trilium context service: ${errorMessage}`);
|
||||
throw error;
|
||||
} finally {
|
||||
this.initPromise = null;
|
||||
}
|
||||
})();
|
||||
|
||||
return this.initPromise;
|
||||
}
|
||||
|
||||
/**
|
||||
* Set up periodic cache cleanup
|
||||
*/
|
||||
private setupCacheCleanup() {
|
||||
setInterval(() => {
|
||||
const now = Date.now();
|
||||
for (const [key, data] of this.recentQueriesCache.entries()) {
|
||||
if (now - data.timestamp > this.cacheExpiryMs) {
|
||||
this.recentQueriesCache.delete(key);
|
||||
}
|
||||
}
|
||||
}, 60000); // Run cleanup every minute
|
||||
}
|
||||
|
||||
/**
|
||||
* Generate search queries to find relevant information for the user question
|
||||
* @param userQuestion - The user's question
|
||||
* @param llmService - The LLM service to use for generating queries
|
||||
* @returns Array of search queries
|
||||
*/
|
||||
async generateSearchQueries(userQuestion: string, llmService: any): Promise<string[]> {
|
||||
try {
|
||||
const messages: Message[] = [
|
||||
{ role: "system", content: this.metaPrompt },
|
||||
{ role: "user", content: userQuestion }
|
||||
];
|
||||
|
||||
const options = {
|
||||
temperature: 0.3,
|
||||
maxTokens: 300
|
||||
};
|
||||
|
||||
// Get the response from the LLM
|
||||
const response = await llmService.sendTextCompletion(messages, options);
|
||||
|
||||
try {
|
||||
// Parse the JSON response
|
||||
const jsonStr = response.trim().replace(/```json|```/g, '').trim();
|
||||
const queries = JSON.parse(jsonStr);
|
||||
|
||||
if (Array.isArray(queries) && queries.length > 0) {
|
||||
return queries;
|
||||
} else {
|
||||
throw new Error("Invalid response format");
|
||||
}
|
||||
} catch (parseError) {
|
||||
// Fallback: if JSON parsing fails, try to extract queries line by line
|
||||
const lines = response.split('\n')
|
||||
.map((line: string) => line.trim())
|
||||
.filter((line: string) => line.length > 0 && !line.startsWith('```'));
|
||||
|
||||
if (lines.length > 0) {
|
||||
return lines.map((line: string) => line.replace(/^["'\d\.\-\s]+/, '').trim());
|
||||
}
|
||||
|
||||
// If all else fails, just use the original question
|
||||
return [userQuestion];
|
||||
}
|
||||
} catch (error: unknown) {
|
||||
const errorMessage = error instanceof Error ? error.message : String(error);
|
||||
log.error(`Error generating search queries: ${errorMessage}`);
|
||||
// Fallback to just using the original question
|
||||
return [userQuestion];
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Find relevant notes using multiple search queries
|
||||
* @param queries - Array of search queries
|
||||
* @param contextNoteId - Optional note ID to restrict search to a branch
|
||||
* @param limit - Max notes to return
|
||||
* @returns Array of relevant notes
|
||||
*/
|
||||
async findRelevantNotesMultiQuery(
|
||||
queries: string[],
|
||||
contextNoteId: string | null = null,
|
||||
limit = 10
|
||||
): Promise<any[]> {
|
||||
if (!this.initialized) {
|
||||
await this.initialize();
|
||||
}
|
||||
|
||||
try {
|
||||
// Cache key combining all queries
|
||||
const cacheKey = JSON.stringify({ queries, contextNoteId, limit });
|
||||
|
||||
// Check if we have a recent cache hit
|
||||
const cached = this.recentQueriesCache.get(cacheKey);
|
||||
if (cached) {
|
||||
return cached.relevantNotes;
|
||||
}
|
||||
|
||||
// Array to store all results with their similarity scores
|
||||
const allResults: {
|
||||
noteId: string,
|
||||
title: string,
|
||||
content: string | null,
|
||||
similarity: number,
|
||||
branchId?: string
|
||||
}[] = [];
|
||||
|
||||
// Set to keep track of note IDs we've seen to avoid duplicates
|
||||
const seenNoteIds = new Set<string>();
|
||||
|
||||
// Process each query
|
||||
for (const query of queries) {
|
||||
// Get embeddings for this query
|
||||
const queryEmbedding = await this.provider.getEmbedding(query);
|
||||
|
||||
// Find notes similar to this query
|
||||
let results;
|
||||
if (contextNoteId) {
|
||||
// Find within a specific context/branch
|
||||
results = await this.findNotesInBranch(
|
||||
queryEmbedding,
|
||||
contextNoteId,
|
||||
Math.min(limit, 5) // Limit per query
|
||||
);
|
||||
} else {
|
||||
// Search all notes
|
||||
results = await vectorStore.findSimilarNotes(
|
||||
queryEmbedding,
|
||||
this.provider.id,
|
||||
this.provider.modelId,
|
||||
Math.min(limit, 5), // Limit per query
|
||||
0.5 // Lower threshold to get more diverse results
|
||||
);
|
||||
}
|
||||
|
||||
// Process results
|
||||
for (const result of results) {
|
||||
if (!seenNoteIds.has(result.noteId)) {
|
||||
seenNoteIds.add(result.noteId);
|
||||
|
||||
// Get the note from Becca
|
||||
const note = becca.notes[result.noteId];
|
||||
if (!note) continue;
|
||||
|
||||
// Add to our results
|
||||
allResults.push({
|
||||
noteId: result.noteId,
|
||||
title: note.title,
|
||||
content: note.type === 'text' ? note.getContent() as string : null,
|
||||
similarity: result.similarity,
|
||||
branchId: note.getBranches()[0]?.branchId
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Sort by similarity and take the top 'limit' results
|
||||
const sortedResults = allResults
|
||||
.sort((a, b) => b.similarity - a.similarity)
|
||||
.slice(0, limit);
|
||||
|
||||
// Cache the results
|
||||
this.recentQueriesCache.set(cacheKey, {
|
||||
timestamp: Date.now(),
|
||||
relevantNotes: sortedResults
|
||||
});
|
||||
|
||||
return sortedResults;
|
||||
} catch (error: unknown) {
|
||||
const errorMessage = error instanceof Error ? error.message : String(error);
|
||||
log.error(`Error finding relevant notes: ${errorMessage}`);
|
||||
return [];
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Find notes in a specific branch/context
|
||||
* @param embedding - Query embedding
|
||||
* @param contextNoteId - Note ID to restrict search to
|
||||
* @param limit - Max notes to return
|
||||
* @returns Array of relevant notes
|
||||
*/
|
||||
private async findNotesInBranch(
|
||||
embedding: Float32Array,
|
||||
contextNoteId: string,
|
||||
limit = 5
|
||||
): Promise<{noteId: string, similarity: number}[]> {
|
||||
try {
|
||||
// Get the subtree note IDs
|
||||
const subtreeNoteIds = await this.getSubtreeNoteIds(contextNoteId);
|
||||
|
||||
if (subtreeNoteIds.length === 0) {
|
||||
return [];
|
||||
}
|
||||
|
||||
// Get all embeddings for these notes using vectorStore instead of direct SQL
|
||||
const similarities: {noteId: string, similarity: number}[] = [];
|
||||
|
||||
for (const noteId of subtreeNoteIds) {
|
||||
const noteEmbedding = await vectorStore.getEmbeddingForNote(
|
||||
noteId,
|
||||
this.provider.id,
|
||||
this.provider.modelId
|
||||
);
|
||||
|
||||
if (noteEmbedding) {
|
||||
const similarity = cosineSimilarity(embedding, noteEmbedding.embedding);
|
||||
if (similarity > 0.5) { // Apply similarity threshold
|
||||
similarities.push({
|
||||
noteId,
|
||||
similarity
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Sort by similarity and return top results
|
||||
return similarities
|
||||
.sort((a, b) => b.similarity - a.similarity)
|
||||
.slice(0, limit);
|
||||
} catch (error: unknown) {
|
||||
const errorMessage = error instanceof Error ? error.message : String(error);
|
||||
log.error(`Error finding notes in branch: ${errorMessage}`);
|
||||
return [];
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Get all note IDs in a subtree (including the root note)
|
||||
* @param rootNoteId - Root note ID
|
||||
* @returns Array of note IDs
|
||||
*/
|
||||
private async getSubtreeNoteIds(rootNoteId: string): Promise<string[]> {
|
||||
const note = becca.notes[rootNoteId];
|
||||
if (!note) {
|
||||
return [];
|
||||
}
|
||||
|
||||
// Use becca to walk the note tree instead of direct SQL
|
||||
const noteIds = new Set<string>([rootNoteId]);
|
||||
|
||||
// Helper function to collect all children
|
||||
const collectChildNotes = (noteId: string) => {
|
||||
// Use becca.getNote(noteId).getChildNotes() to get child notes
|
||||
const parentNote = becca.notes[noteId];
|
||||
if (!parentNote) return;
|
||||
|
||||
// Get all branches where this note is the parent
|
||||
for (const branch of Object.values(becca.branches)) {
|
||||
if (branch.parentNoteId === noteId && !branch.isDeleted) {
|
||||
const childNoteId = branch.noteId;
|
||||
if (!noteIds.has(childNoteId)) {
|
||||
noteIds.add(childNoteId);
|
||||
// Recursively collect children of this child
|
||||
collectChildNotes(childNoteId);
|
||||
}
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
// Start collecting from the root
|
||||
collectChildNotes(rootNoteId);
|
||||
|
||||
return Array.from(noteIds);
|
||||
}
|
||||
|
||||
/**
|
||||
* Build a context string from relevant notes
|
||||
* @param sources - Array of notes
|
||||
* @param query - Original user query
|
||||
* @returns Formatted context string
|
||||
*/
|
||||
buildContextFromNotes(sources: any[], query: string): string {
|
||||
if (!sources || sources.length === 0) {
|
||||
return "";
|
||||
}
|
||||
|
||||
let context = `The following are relevant notes from your knowledge base that may help answer the query: "${query}"\n\n`;
|
||||
|
||||
sources.forEach((source, index) => {
|
||||
context += `--- NOTE ${index + 1}: ${source.title} ---\n`;
|
||||
|
||||
if (source.content) {
|
||||
// Truncate content if it's too long
|
||||
const maxContentLength = 1000;
|
||||
let content = source.content;
|
||||
|
||||
if (content.length > maxContentLength) {
|
||||
content = content.substring(0, maxContentLength) + " [content truncated due to length]";
|
||||
}
|
||||
|
||||
context += `${content}\n`;
|
||||
} else {
|
||||
context += "[This note doesn't contain textual content]\n";
|
||||
}
|
||||
|
||||
context += "\n";
|
||||
});
|
||||
|
||||
context += "--- END OF NOTES ---\n\n";
|
||||
context += "Please use the information above to help answer the query. If the information doesn't contain what you need, just say so and use your general knowledge instead.";
|
||||
|
||||
return context;
|
||||
}
|
||||
|
||||
/**
|
||||
* Process a user query with the Trilium-specific approach:
|
||||
* 1. Generate search queries from the original question
|
||||
* 2. Find relevant notes using those queries
|
||||
* 3. Build a context string from the relevant notes
|
||||
*
|
||||
* @param userQuestion - The user's original question
|
||||
* @param llmService - The LLM service to use
|
||||
* @param contextNoteId - Optional note ID to restrict search to
|
||||
* @returns Object with context and notes
|
||||
*/
|
||||
async processQuery(userQuestion: string, llmService: any, contextNoteId: string | null = null) {
|
||||
if (!this.initialized) {
|
||||
await this.initialize();
|
||||
}
|
||||
|
||||
// Step 1: Generate search queries
|
||||
const searchQueries = await this.generateSearchQueries(userQuestion, llmService);
|
||||
log.info(`Generated search queries: ${JSON.stringify(searchQueries)}`);
|
||||
|
||||
// Step 2: Find relevant notes using those queries
|
||||
const relevantNotes = await this.findRelevantNotesMultiQuery(
|
||||
searchQueries,
|
||||
contextNoteId,
|
||||
8 // Get more notes since we're using multiple queries
|
||||
);
|
||||
|
||||
// Step 3: Build context from the notes
|
||||
const context = this.buildContextFromNotes(relevantNotes, userQuestion);
|
||||
|
||||
return {
|
||||
context,
|
||||
notes: relevantNotes,
|
||||
queries: searchQueries
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
export default new TriliumContextService();
|
||||
Loading…
x
Reference in New Issue
Block a user