mirror of
https://github.com/zadam/trilium.git
synced 2025-11-03 21:19:01 +01:00
fancier (but longer waiting time) messages
This commit is contained in:
perf3ct
parent
56fc720ac7
commit
4160db9728
@ -4,6 +4,8 @@ import { OpenAIService } from './providers/openai_service.js';
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import { AnthropicService } from './providers/anthropic_service.js';
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import { OllamaService } from './providers/ollama_service.js';
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import log from '../log.js';
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import contextExtractor from './context_extractor.js';
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import semanticContextService from './semantic_context_service.js';
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type ServiceProviders = 'openai' | 'anthropic' | 'ollama';
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@ -159,6 +161,26 @@ export class AIServiceManager {
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// If we get here, all providers failed
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throw new Error(`All AI providers failed: ${lastError?.message || 'Unknown error'}`);
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}
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setupEventListeners() {
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// Setup event listeners for AI services
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}
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/**
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* Get the context extractor service
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* @returns The context extractor instance
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*/
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getContextExtractor() {
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return contextExtractor;
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}
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/**
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* Get the semantic context service for advanced context handling
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* @returns The semantic context service instance
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*/
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getSemanticContextService() {
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return semanticContextService;
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}
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}
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// Don't create singleton immediately, use a lazy-loading pattern
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@ -185,5 +207,12 @@ export default {
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},
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async generateChatCompletion(messages: Message[], options: ChatCompletionOptions = {}): Promise<ChatResponse> {
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return getInstance().generateChatCompletion(messages, options);
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},
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// Add our new methods
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getContextExtractor() {
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return getInstance().getContextExtractor();
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},
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getSemanticContextService() {
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return getInstance().getSemanticContextService();
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}
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};
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@ -152,10 +152,28 @@ export class ChatService {
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/**
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* Add context from the current note to the chat
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*
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* @param sessionId - The ID of the chat session
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* @param noteId - The ID of the note to add context from
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* @param useSmartContext - Whether to use smart context extraction (default: true)
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* @returns The updated chat session
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*/
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async addNoteContext(sessionId: string, noteId: string): Promise<ChatSession> {
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async addNoteContext(sessionId: string, noteId: string, useSmartContext = true): Promise<ChatSession> {
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const session = await this.getOrCreateSession(sessionId);
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const context = await contextExtractor.getFullContext(noteId);
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// Get the last user message to use as context for semantic search
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const lastUserMessage = [...session.messages].reverse()
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.find(msg => msg.role === 'user' && msg.content.length > 10)?.content || '';
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let context;
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if (useSmartContext && lastUserMessage) {
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// Use smart context that considers the query for better relevance
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context = await contextExtractor.getSmartContext(noteId, lastUserMessage);
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} else {
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// Fall back to full context if smart context is disabled or no query available
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context = await contextExtractor.getFullContext(noteId);
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}
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const contextMessage: Message = {
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role: 'user',
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@ -168,6 +186,61 @@ export class ChatService {
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return session;
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}
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/**
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* Add semantically relevant context from a note based on a specific query
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*
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* @param sessionId - The ID of the chat session
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* @param noteId - The ID of the note to add context from
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* @param query - The specific query to find relevant information for
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* @returns The updated chat session
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*/
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async addSemanticNoteContext(sessionId: string, noteId: string, query: string): Promise<ChatSession> {
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const session = await this.getOrCreateSession(sessionId);
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// Use semantic context that considers the query for better relevance
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const context = await contextExtractor.getSemanticContext(noteId, query);
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const contextMessage: Message = {
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role: 'user',
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content: `Here is the relevant information from my notes based on my query "${query}":\n\n${context}\n\nPlease help me understand this information in relation to my query.`
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};
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session.messages.push(contextMessage);
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await chatStorageService.updateChat(session.id, session.messages);
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return session;
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}
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/**
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* Send a context-aware message with automatically included semantic context from a note
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* This method combines the query with relevant note context before sending to the AI
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*
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* @param sessionId - The ID of the chat session
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* @param content - The user's message content
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* @param noteId - The ID of the note to add context from
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* @param options - Optional completion options
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* @param streamCallback - Optional streaming callback
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* @returns The updated chat session
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*/
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async sendContextAwareMessage(
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sessionId: string,
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content: string,
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noteId: string,
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options?: ChatCompletionOptions,
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streamCallback?: (content: string, isDone: boolean) => void
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): Promise<ChatSession> {
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const session = await this.getOrCreateSession(sessionId);
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// Get semantically relevant context based on the user's message
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const context = await contextExtractor.getSmartContext(noteId, content);
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// Combine the user's message with the relevant context
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const enhancedContent = `${content}\n\nHere's relevant information from my notes that may help:\n\n${context}`;
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// Send the enhanced message
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return this.sendMessage(sessionId, enhancedContent, options, streamCallback);
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}
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/**
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* Get all user's chat sessions
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*/
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@ -3,6 +3,7 @@ import sanitizeHtml from 'sanitize-html';
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/**
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* Utility class for extracting context from notes to provide to AI models
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* Enhanced with advanced capabilities for handling large notes and specialized content
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*/
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export class ContextExtractor {
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/**
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@ -24,6 +25,158 @@ export class ContextExtractor {
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return this.formatNoteContent(note.content, note.type, note.mime, note.title);
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}
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/**
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* Split a large note into smaller, semantically meaningful chunks
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* This is useful for handling large notes that exceed the context window of LLMs
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*
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* @param noteId - The ID of the note to chunk
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* @param maxChunkSize - Maximum size of each chunk in characters
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* @returns Array of content chunks, or empty array if note not found
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*/
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async getChunkedNoteContent(noteId: string, maxChunkSize = 2000): Promise<string[]> {
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const content = await this.getNoteContent(noteId);
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if (!content) return [];
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// Split into semantic chunks (paragraphs, sections, etc.)
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return this.splitContentIntoChunks(content, maxChunkSize);
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}
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/**
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* Split text content into semantically meaningful chunks based on natural boundaries
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* like paragraphs, headings, and code blocks
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*
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* @param content - The text content to split
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* @param maxChunkSize - Maximum size of each chunk in characters
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* @returns Array of content chunks
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*/
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private splitContentIntoChunks(content: string, maxChunkSize: number): string[] {
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// Look for semantic boundaries (headings, blank lines, etc.)
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const headingPattern = /^(#+)\s+(.+)$/gm;
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const codeBlockPattern = /```[\s\S]+?```/gm;
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// Replace code blocks with placeholders to avoid splitting inside them
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const codeBlocks: string[] = [];
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let contentWithPlaceholders = content.replace(codeBlockPattern, (match) => {
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const placeholder = `__CODE_BLOCK_${codeBlocks.length}__`;
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codeBlocks.push(match);
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return placeholder;
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});
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// Split content at headings and paragraphs
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const sections: string[] = [];
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let currentSection = '';
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// First split by headings
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const lines = contentWithPlaceholders.split('\n');
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for (const line of lines) {
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const isHeading = headingPattern.test(line);
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headingPattern.lastIndex = 0; // Reset regex
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// If this is a heading and we already have content, start a new section
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if (isHeading && currentSection.trim().length > 0) {
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sections.push(currentSection.trim());
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currentSection = line;
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} else {
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currentSection += (currentSection ? '\n' : '') + line;
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}
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}
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// Add the last section if there's any content
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if (currentSection.trim().length > 0) {
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sections.push(currentSection.trim());
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}
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// Now combine smaller sections to respect maxChunkSize
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const chunks: string[] = [];
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let currentChunk = '';
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for (const section of sections) {
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// If adding this section exceeds maxChunkSize and we already have content,
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// finalize the current chunk and start a new one
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if ((currentChunk + section).length > maxChunkSize && currentChunk.length > 0) {
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chunks.push(currentChunk);
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currentChunk = section;
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} else {
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currentChunk += (currentChunk ? '\n\n' : '') + section;
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}
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}
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// Add the last chunk if there's any content
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if (currentChunk.length > 0) {
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chunks.push(currentChunk);
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}
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// Restore code blocks in all chunks
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return chunks.map(chunk => {
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return chunk.replace(/__CODE_BLOCK_(\d+)__/g, (_, index) => {
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return codeBlocks[parseInt(index)];
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});
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});
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}
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/**
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* Generate a summary of a note's content
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* Useful for providing a condensed version of very large notes
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*
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* @param noteId - The ID of the note to summarize
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* @param maxLength - Cut-off length to trigger summarization
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* @returns Summary of the note or the original content if small enough
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*/
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async getNoteSummary(noteId: string, maxLength = 5000): Promise<string> {
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const content = await this.getNoteContent(noteId);
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if (!content || content.length < maxLength) return content || '';
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// For larger content, generate a summary
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return this.summarizeContent(content);
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}
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/**
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* Summarize content by extracting key information
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* This uses a heuristic approach to find important sentences and paragraphs
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*
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* @param content - The content to summarize
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* @returns A summarized version of the content
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*/
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private summarizeContent(content: string): string {
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// Extract title/heading if present
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const titleMatch = content.match(/^# (.+)$/m);
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const title = titleMatch ? titleMatch[1] : 'Untitled Note';
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// Extract all headings for an outline
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const headings: string[] = [];
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const headingMatches = content.matchAll(/^(#+)\s+(.+)$/gm);
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for (const match of headingMatches) {
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const level = match[1].length;
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const text = match[2];
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headings.push(`${' '.repeat(level-1)}- ${text}`);
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}
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// Extract first sentence of each paragraph for a summary
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const paragraphs = content.split(/\n\s*\n/);
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const firstSentences = paragraphs
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.filter(p => p.trim().length > 0 && !p.trim().startsWith('#') && !p.trim().startsWith('```'))
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.map(p => {
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const sentenceMatch = p.match(/^[^.!?]+[.!?]/);
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return sentenceMatch ? sentenceMatch[0].trim() : p.substring(0, Math.min(150, p.length)).trim() + '...';
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})
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.slice(0, 5); // Limit to 5 sentences
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// Create the summary
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let summary = `# Summary of: ${title}\n\n`;
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if (headings.length > 0) {
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summary += `## Document Outline\n${headings.join('\n')}\n\n`;
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}
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if (firstSentences.length > 0) {
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summary += `## Key Points\n${firstSentences.map(s => `- ${s}`).join('\n')}\n\n`;
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}
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summary += `(Note: This is an automatically generated summary of a larger document with ${content.length} characters)`;
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return summary;
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}
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/**
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* Get a set of parent notes to provide hierarchical context
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*/
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@ -89,6 +242,7 @@ export class ContextExtractor {
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/**
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* Format the content of a note based on its type
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* Enhanced with better handling for large and specialized content types
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*/
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private formatNoteContent(content: string, type: string, mime: string, title: string): string {
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let formattedContent = `# ${title}\n\n`;
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@ -98,10 +252,19 @@ export class ContextExtractor {
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// Remove HTML formatting for text notes
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formattedContent += this.sanitizeHtml(content);
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break;
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case 'code':
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// Format code notes with code blocks
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formattedContent += '```\n' + content + '\n```';
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// Improved code handling with language detection
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const codeLanguage = this.detectCodeLanguage(content, mime);
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// For large code files, extract structure rather than full content
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if (content.length > 8000) {
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formattedContent += this.extractCodeStructure(content, codeLanguage);
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} else {
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formattedContent += `\`\`\`${codeLanguage}\n${content}\n\`\`\``;
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}
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break;
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case 'canvas':
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if (mime === 'application/json') {
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try {
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@ -249,6 +412,230 @@ export class ContextExtractor {
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return formattedContent;
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}
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/**
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* Detect the programming language of code content
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*
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* @param content - The code content to analyze
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* @param mime - MIME type (if available)
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* @returns The detected language or empty string
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*/
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private detectCodeLanguage(content: string, mime: string): string {
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// First check if mime type provides a hint
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if (mime) {
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const mimeMap: Record<string, string> = {
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'text/x-python': 'python',
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'text/javascript': 'javascript',
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'application/javascript': 'javascript',
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'text/typescript': 'typescript',
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'application/typescript': 'typescript',
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'text/x-java': 'java',
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'text/html': 'html',
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'text/css': 'css',
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'text/x-c': 'c',
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'text/x-c++': 'cpp',
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'text/x-csharp': 'csharp',
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'text/x-go': 'go',
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'text/x-ruby': 'ruby',
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'text/x-php': 'php',
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'text/x-swift': 'swift',
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'text/x-rust': 'rust',
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'text/markdown': 'markdown',
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'text/x-sql': 'sql',
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'text/x-yaml': 'yaml',
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'application/json': 'json',
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'text/x-shell': 'bash'
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};
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for (const [mimePattern, language] of Object.entries(mimeMap)) {
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if (mime.includes(mimePattern)) {
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return language;
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}
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}
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}
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// Check for common language patterns in the content
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const firstLines = content.split('\n', 20).join('\n');
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const languagePatterns: Record<string, RegExp> = {
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'python': /^(import\s+|from\s+\w+\s+import|def\s+\w+\s*\(|class\s+\w+\s*:)/m,
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'javascript': /^(const\s+\w+\s*=|let\s+\w+\s*=|var\s+\w+\s*=|function\s+\w+\s*\(|import\s+.*from\s+)/m,
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'typescript': /^(interface\s+\w+|type\s+\w+\s*=|class\s+\w+\s*{)/m,
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'html': /^<!DOCTYPE html>|<html>|<head>|<body>/m,
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'css': /^(\.\w+\s*{|\#\w+\s*{|@media|@import)/m,
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'java': /^(public\s+class|import\s+java|package\s+)/m,
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'cpp': /^(#include\s+<\w+>|namespace\s+\w+|void\s+\w+\s*\()/m,
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'csharp': /^(using\s+System|namespace\s+\w+|public\s+class)/m,
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'go': /^(package\s+\w+|import\s+\(|func\s+\w+\s*\()/m,
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'ruby': /^(require\s+|class\s+\w+\s*<|def\s+\w+)/m,
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'php': /^(<\?php|namespace\s+\w+|use\s+\w+)/m,
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'sql': /^(SELECT|INSERT|UPDATE|DELETE|CREATE TABLE|ALTER TABLE)/im,
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'bash': /^(#!\/bin\/sh|#!\/bin\/bash|function\s+\w+\s*\(\))/m,
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'markdown': /^(#\s+|##\s+|###\s+|\*\s+|-\s+|>\s+)/m,
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'json': /^({[\s\n]*"|[\s\n]*\[)/m,
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'yaml': /^(---|\w+:\s+)/m
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};
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for (const [language, pattern] of Object.entries(languagePatterns)) {
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if (pattern.test(firstLines)) {
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return language;
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}
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}
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// Default to empty string if we can't detect the language
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return '';
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}
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/**
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* Extract the structure of a code file rather than its full content
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* Useful for providing high-level understanding of large code files
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*
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* @param content - The full code content
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* @param language - The programming language
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* @returns A structured representation of the code
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*/
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private extractCodeStructure(content: string, language: string): string {
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const lines = content.split('\n');
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const maxLines = 8000;
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// If it's not that much over the limit, just include the whole thing
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if (lines.length <= maxLines * 1.2) {
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return `\`\`\`${language}\n${content}\n\`\`\``;
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}
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// For large files, extract important structural elements based on language
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let extractedStructure = '';
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let importSection = '';
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let classDefinitions = [];
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let functionDefinitions = [];
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let otherImportantLines = [];
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// Extract imports/includes, class/function definitions based on language
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if (['javascript', 'typescript', 'python', 'java', 'csharp'].includes(language)) {
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// Find imports
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for (let i = 0; i < Math.min(100, lines.length); i++) {
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if (lines[i].match(/^(import|from|using|require|#include|package)\s+/)) {
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importSection += lines[i] + '\n';
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}
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}
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// Find class definitions
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for (let i = 0; i < lines.length; i++) {
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if (lines[i].match(/^(class|interface|type)\s+\w+/)) {
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const endBracketLine = this.findMatchingEnd(lines, i, language);
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if (endBracketLine > i && endBracketLine <= i + 10) {
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// Include small class definitions entirely
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classDefinitions.push(lines.slice(i, endBracketLine + 1).join('\n'));
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i = endBracketLine;
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} else {
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// For larger classes, just show the definition and methods
|
||||
let className = lines[i];
|
||||
classDefinitions.push(className);
|
||||
|
||||
// Look for methods in this class
|
||||
for (let j = i + 1; j < Math.min(endBracketLine, lines.length); j++) {
|
||||
if (lines[j].match(/^\s+(function|def|public|private|protected)\s+\w+/)) {
|
||||
classDefinitions.push(' ' + lines[j].trim());
|
||||
}
|
||||
}
|
||||
|
||||
if (endBracketLine > 0 && endBracketLine < lines.length) {
|
||||
i = endBracketLine;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Find function definitions not inside classes
|
||||
for (let i = 0; i < lines.length; i++) {
|
||||
if (lines[i].match(/^(function|def|const\s+\w+\s*=\s*\(|let\s+\w+\s*=\s*\(|var\s+\w+\s*=\s*\()/)) {
|
||||
functionDefinitions.push(lines[i]);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Build the extracted structure
|
||||
extractedStructure += `# Code Structure (${lines.length} lines total)\n\n`;
|
||||
|
||||
if (importSection) {
|
||||
extractedStructure += "## Imports/Dependencies\n```" + language + "\n" + importSection + "```\n\n";
|
||||
}
|
||||
|
||||
if (classDefinitions.length > 0) {
|
||||
extractedStructure += "## Classes/Interfaces\n```" + language + "\n" + classDefinitions.join('\n\n') + "\n```\n\n";
|
||||
}
|
||||
|
||||
if (functionDefinitions.length > 0) {
|
||||
extractedStructure += "## Functions\n```" + language + "\n" + functionDefinitions.join('\n\n') + "\n```\n\n";
|
||||
}
|
||||
|
||||
// Add beginning and end of the file for context
|
||||
extractedStructure += "## Beginning of File\n```" + language + "\n" +
|
||||
lines.slice(0, Math.min(50, lines.length)).join('\n') + "\n```\n\n";
|
||||
|
||||
if (lines.length > 100) {
|
||||
extractedStructure += "## End of File\n```" + language + "\n" +
|
||||
lines.slice(Math.max(0, lines.length - 50)).join('\n') + "\n```\n\n";
|
||||
}
|
||||
|
||||
return extractedStructure;
|
||||
}
|
||||
|
||||
/**
|
||||
* Find the line number of the matching ending bracket/block
|
||||
*
|
||||
* @param lines - Array of code lines
|
||||
* @param startLine - Starting line number
|
||||
* @param language - Programming language
|
||||
* @returns The line number of the matching end, or -1 if not found
|
||||
*/
|
||||
private findMatchingEnd(lines: string[], startLine: number, language: string): number {
|
||||
let depth = 0;
|
||||
let inClass = false;
|
||||
|
||||
// Different languages have different ways to define blocks
|
||||
if (['javascript', 'typescript', 'java', 'csharp', 'cpp'].includes(language)) {
|
||||
// Curly brace languages
|
||||
for (let i = startLine; i < lines.length; i++) {
|
||||
const line = lines[i];
|
||||
// Count opening braces
|
||||
for (const char of line) {
|
||||
if (char === '{') depth++;
|
||||
if (char === '}') {
|
||||
depth--;
|
||||
if (depth === 0 && inClass) return i;
|
||||
}
|
||||
}
|
||||
|
||||
// Check if this line contains the class declaration
|
||||
if (i === startLine && line.includes('{')) {
|
||||
inClass = true;
|
||||
} else if (i === startLine) {
|
||||
// If the first line doesn't have an opening brace, look at the next few lines
|
||||
if (i + 1 < lines.length && lines[i + 1].includes('{')) {
|
||||
inClass = true;
|
||||
}
|
||||
}
|
||||
}
|
||||
} else if (language === 'python') {
|
||||
// Indentation-based language
|
||||
const baseIndentation = lines[startLine].match(/^\s*/)?.[0].length || 0;
|
||||
|
||||
for (let i = startLine + 1; i < lines.length; i++) {
|
||||
// Skip empty lines
|
||||
if (lines[i].trim() === '') continue;
|
||||
|
||||
const currentIndentation = lines[i].match(/^\s*/)?.[0].length || 0;
|
||||
|
||||
// If we're back to the same or lower indentation level, we've reached the end
|
||||
if (currentIndentation <= baseIndentation) {
|
||||
return i - 1;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return -1;
|
||||
}
|
||||
|
||||
/**
|
||||
* Sanitize HTML content to plain text
|
||||
*/
|
||||
@ -328,6 +715,88 @@ export class ContextExtractor {
|
||||
linkedContext
|
||||
].filter(Boolean).join('\n\n');
|
||||
}
|
||||
|
||||
/**
|
||||
* Get semantically ranked context based on semantic similarity to a query
|
||||
* This method delegates to the semantic context service for the actual ranking
|
||||
*
|
||||
* @param noteId - The ID of the current note
|
||||
* @param query - The user's query to compare against
|
||||
* @param maxResults - Maximum number of related notes to include
|
||||
* @returns Context with the most semantically relevant related notes
|
||||
*/
|
||||
async getSemanticContext(noteId: string, query: string, maxResults = 5): Promise<string> {
|
||||
try {
|
||||
// This requires the semantic context service to be available
|
||||
// We're using a dynamic import to avoid circular dependencies
|
||||
const { default: aiServiceManager } = await import('./ai_service_manager.js');
|
||||
const semanticContext = aiServiceManager.getInstance().getSemanticContextService();
|
||||
|
||||
if (!semanticContext) {
|
||||
return this.getFullContext(noteId);
|
||||
}
|
||||
|
||||
return await semanticContext.getSemanticContext(noteId, query, maxResults);
|
||||
} catch (error) {
|
||||
// Fall back to regular context if semantic ranking fails
|
||||
console.error('Error in semantic context ranking:', error);
|
||||
return this.getFullContext(noteId);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Get progressively loaded context based on depth level
|
||||
* This provides different levels of context detail depending on the depth parameter
|
||||
*
|
||||
* @param noteId - The ID of the note to get context for
|
||||
* @param depth - Depth level (1-4) determining how much context to include
|
||||
* @returns Context appropriate for the requested depth
|
||||
*/
|
||||
async getProgressiveContext(noteId: string, depth = 1): Promise<string> {
|
||||
try {
|
||||
// This requires the semantic context service to be available
|
||||
// We're using a dynamic import to avoid circular dependencies
|
||||
const { default: aiServiceManager } = await import('./ai_service_manager.js');
|
||||
const semanticContext = aiServiceManager.getInstance().getSemanticContextService();
|
||||
|
||||
if (!semanticContext) {
|
||||
return this.getFullContext(noteId);
|
||||
}
|
||||
|
||||
return await semanticContext.getProgressiveContext(noteId, depth);
|
||||
} catch (error) {
|
||||
// Fall back to regular context if progressive loading fails
|
||||
console.error('Error in progressive context loading:', error);
|
||||
return this.getFullContext(noteId);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Get smart context based on the query complexity
|
||||
* This automatically selects the appropriate context depth and relevance
|
||||
*
|
||||
* @param noteId - The ID of the note to get context for
|
||||
* @param query - The user's query for semantic relevance matching
|
||||
* @returns The optimal context for answering the query
|
||||
*/
|
||||
async getSmartContext(noteId: string, query: string): Promise<string> {
|
||||
try {
|
||||
// This requires the semantic context service to be available
|
||||
// We're using a dynamic import to avoid circular dependencies
|
||||
const { default: aiServiceManager } = await import('./ai_service_manager.js');
|
||||
const semanticContext = aiServiceManager.getInstance().getSemanticContextService();
|
||||
|
||||
if (!semanticContext) {
|
||||
return this.getFullContext(noteId);
|
||||
}
|
||||
|
||||
return await semanticContext.getSmartContext(noteId, query);
|
||||
} catch (error) {
|
||||
// Fall back to regular context if smart context fails
|
||||
console.error('Error in smart context selection:', error);
|
||||
return this.getFullContext(noteId);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Singleton instance
|
||||
|
||||
401
src/services/llm/semantic_context_service.ts
Normal file
401
src/services/llm/semantic_context_service.ts
Normal file
@ -0,0 +1,401 @@
|
||||
import contextExtractor from './context_extractor.js';
|
||||
import * as vectorStore from './embeddings/vector_store.js';
|
||||
import sql from '../sql.js';
|
||||
import { cosineSimilarity } from './embeddings/vector_store.js';
|
||||
import log from '../log.js';
|
||||
import { getEmbeddingProvider, getEnabledEmbeddingProviders } from './embeddings/providers.js';
|
||||
import options from '../options.js';
|
||||
|
||||
/**
|
||||
* SEMANTIC CONTEXT SERVICE
|
||||
*
|
||||
* This service provides advanced context extraction capabilities for AI models.
|
||||
* It enhances the basic context extractor with vector embedding-based semantic
|
||||
* search and progressive context loading for large notes.
|
||||
*
|
||||
* === USAGE GUIDE ===
|
||||
*
|
||||
* 1. To use this service in other modules:
|
||||
* ```
|
||||
* import aiServiceManager from './services/llm/ai_service_manager.js';
|
||||
* const semanticContext = aiServiceManager.getSemanticContextService();
|
||||
* ```
|
||||
*
|
||||
* Or with the instance directly:
|
||||
* ```
|
||||
* import aiServiceManager from './services/llm/ai_service_manager.js';
|
||||
* const semanticContext = aiServiceManager.getInstance().getSemanticContextService();
|
||||
* ```
|
||||
*
|
||||
* 2. Retrieve context based on semantic relevance to a query:
|
||||
* ```
|
||||
* const context = await semanticContext.getSemanticContext(noteId, userQuery);
|
||||
* ```
|
||||
*
|
||||
* 3. Load context progressively (only what's needed):
|
||||
* ```
|
||||
* const context = await semanticContext.getProgressiveContext(noteId, depth);
|
||||
* // depth: 1=just note, 2=+parents, 3=+children, 4=+linked notes
|
||||
* ```
|
||||
*
|
||||
* 4. Use smart context selection that adapts to query complexity:
|
||||
* ```
|
||||
* const context = await semanticContext.getSmartContext(noteId, userQuery);
|
||||
* ```
|
||||
*
|
||||
* === REQUIREMENTS ===
|
||||
*
|
||||
* - Requires at least one configured embedding provider (OpenAI, Anthropic, Ollama)
|
||||
* - Will fall back to non-semantic methods if no embedding provider is available
|
||||
* - Uses OpenAI embeddings by default if API key is configured
|
||||
*/
|
||||
|
||||
/**
|
||||
* Provides advanced semantic context capabilities, enhancing the basic context extractor
|
||||
* with vector embedding-based semantic search and progressive context loading.
|
||||
*
|
||||
* This service is especially useful for retrieving the most relevant context from large
|
||||
* knowledge bases when working with limited-context LLMs.
|
||||
*/
|
||||
class SemanticContextService {
|
||||
/**
|
||||
* Get the preferred embedding provider based on user settings
|
||||
* Tries to use the most appropriate provider in this order:
|
||||
* 1. OpenAI if API key is set
|
||||
* 2. Anthropic if API key is set
|
||||
* 3. Ollama if configured
|
||||
* 4. Any available provider
|
||||
* 5. Local provider as fallback
|
||||
*
|
||||
* @returns The preferred embedding provider or null if none available
|
||||
*/
|
||||
private async getPreferredEmbeddingProvider(): Promise<any> {
|
||||
// Try to get provider in order of preference
|
||||
const openaiKey = await options.getOption('openaiApiKey');
|
||||
if (openaiKey) {
|
||||
const provider = await getEmbeddingProvider('openai');
|
||||
if (provider) return provider;
|
||||
}
|
||||
|
||||
const anthropicKey = await options.getOption('anthropicApiKey');
|
||||
if (anthropicKey) {
|
||||
const provider = await getEmbeddingProvider('anthropic');
|
||||
if (provider) return provider;
|
||||
}
|
||||
|
||||
// If neither of the preferred providers is available, get any provider
|
||||
const providers = await getEnabledEmbeddingProviders();
|
||||
if (providers.length > 0) {
|
||||
return providers[0];
|
||||
}
|
||||
|
||||
// Last resort is local provider
|
||||
return await getEmbeddingProvider('local');
|
||||
}
|
||||
|
||||
/**
|
||||
* Generate embeddings for a text query
|
||||
*
|
||||
* @param query - The text query to embed
|
||||
* @returns The generated embedding or null if failed
|
||||
*/
|
||||
private async generateQueryEmbedding(query: string): Promise<Float32Array | null> {
|
||||
try {
|
||||
// Get the preferred embedding provider
|
||||
const provider = await this.getPreferredEmbeddingProvider();
|
||||
if (!provider) {
|
||||
return null;
|
||||
}
|
||||
return await provider.generateEmbeddings(query);
|
||||
} catch (error) {
|
||||
log.error(`Error generating query embedding: ${error}`);
|
||||
return null;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Rank notes by semantic relevance to a query using vector similarity
|
||||
*
|
||||
* @param notes - Array of notes with noteId and title
|
||||
* @param userQuery - The user's query to compare against
|
||||
* @returns Sorted array of notes with relevance score
|
||||
*/
|
||||
async rankNotesByRelevance(
|
||||
notes: Array<{noteId: string, title: string}>,
|
||||
userQuery: string
|
||||
): Promise<Array<{noteId: string, title: string, relevance: number}>> {
|
||||
const queryEmbedding = await this.generateQueryEmbedding(userQuery);
|
||||
if (!queryEmbedding) {
|
||||
// If embedding fails, return notes in original order
|
||||
return notes.map(note => ({ ...note, relevance: 0 }));
|
||||
}
|
||||
|
||||
const provider = await this.getPreferredEmbeddingProvider();
|
||||
if (!provider) {
|
||||
return notes.map(note => ({ ...note, relevance: 0 }));
|
||||
}
|
||||
|
||||
const rankedNotes = [];
|
||||
|
||||
for (const note of notes) {
|
||||
// Get note embedding from vector store or generate it if not exists
|
||||
let noteEmbedding = null;
|
||||
try {
|
||||
const embeddingResult = await vectorStore.getEmbeddingForNote(
|
||||
note.noteId,
|
||||
provider.name,
|
||||
provider.getConfig().model || ''
|
||||
);
|
||||
|
||||
if (embeddingResult) {
|
||||
noteEmbedding = embeddingResult.embedding;
|
||||
}
|
||||
} catch (error) {
|
||||
log.error(`Error retrieving embedding for note ${note.noteId}: ${error}`);
|
||||
}
|
||||
|
||||
if (!noteEmbedding) {
|
||||
// If note doesn't have an embedding yet, get content and generate one
|
||||
const content = await contextExtractor.getNoteContent(note.noteId);
|
||||
if (content && provider) {
|
||||
try {
|
||||
noteEmbedding = await provider.generateEmbeddings(content);
|
||||
// Store the embedding for future use
|
||||
await vectorStore.storeNoteEmbedding(
|
||||
note.noteId,
|
||||
provider.name,
|
||||
provider.getConfig().model || '',
|
||||
noteEmbedding
|
||||
);
|
||||
} catch (error) {
|
||||
log.error(`Error generating embedding for note ${note.noteId}: ${error}`);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
let relevance = 0;
|
||||
if (noteEmbedding) {
|
||||
// Calculate cosine similarity between query and note
|
||||
relevance = cosineSimilarity(queryEmbedding, noteEmbedding);
|
||||
}
|
||||
|
||||
rankedNotes.push({
|
||||
...note,
|
||||
relevance
|
||||
});
|
||||
}
|
||||
|
||||
// Sort by relevance (highest first)
|
||||
return rankedNotes.sort((a, b) => b.relevance - a.relevance);
|
||||
}
|
||||
|
||||
/**
|
||||
* Retrieve semantic context based on relevance to user query
|
||||
* Finds the most semantically similar notes to the user's query
|
||||
*
|
||||
* @param noteId - Base note ID to start the search from
|
||||
* @param userQuery - Query to find relevant context for
|
||||
* @param maxResults - Maximum number of notes to include in context
|
||||
* @returns Formatted context with the most relevant notes
|
||||
*/
|
||||
async getSemanticContext(noteId: string, userQuery: string, maxResults = 5): Promise<string> {
|
||||
// Get related notes (parents, children, linked notes)
|
||||
const [
|
||||
parentNotes,
|
||||
childNotes,
|
||||
linkedNotes
|
||||
] = await Promise.all([
|
||||
this.getParentNotes(noteId, 3),
|
||||
this.getChildNotes(noteId, 10),
|
||||
this.getLinkedNotes(noteId, 10)
|
||||
]);
|
||||
|
||||
// Combine all related notes
|
||||
const allRelatedNotes = [...parentNotes, ...childNotes, ...linkedNotes];
|
||||
|
||||
// If no related notes, return empty context
|
||||
if (allRelatedNotes.length === 0) {
|
||||
return '';
|
||||
}
|
||||
|
||||
// Rank notes by relevance to query
|
||||
const rankedNotes = await this.rankNotesByRelevance(allRelatedNotes, userQuery);
|
||||
|
||||
// Get content for the top N most relevant notes
|
||||
const mostRelevantNotes = rankedNotes.slice(0, maxResults);
|
||||
const relevantContent = await Promise.all(
|
||||
mostRelevantNotes.map(async note => {
|
||||
const content = await contextExtractor.getNoteContent(note.noteId);
|
||||
if (!content) return null;
|
||||
|
||||
// Format with relevance score and title
|
||||
return `### ${note.title} (Relevance: ${Math.round(note.relevance * 100)}%)\n\n${content}`;
|
||||
})
|
||||
);
|
||||
|
||||
// If no content retrieved, return empty string
|
||||
if (!relevantContent.filter(Boolean).length) {
|
||||
return '';
|
||||
}
|
||||
|
||||
return `# Relevant Context\n\nThe following notes are most relevant to your query:\n\n${
|
||||
relevantContent.filter(Boolean).join('\n\n---\n\n')
|
||||
}`;
|
||||
}
|
||||
|
||||
/**
|
||||
* Load context progressively based on depth level
|
||||
* This allows starting with minimal context and expanding as needed
|
||||
*
|
||||
* @param noteId - The ID of the note to get context for
|
||||
* @param depth - Depth level (1-4) determining how much context to include
|
||||
* @returns Context appropriate for the requested depth
|
||||
*/
|
||||
async getProgressiveContext(noteId: string, depth = 1): Promise<string> {
|
||||
// Start with the note content
|
||||
const noteContent = await contextExtractor.getNoteContent(noteId);
|
||||
if (!noteContent) return 'Note not found';
|
||||
|
||||
// If depth is 1, just return the note content
|
||||
if (depth <= 1) return noteContent;
|
||||
|
||||
// Add parent context for depth >= 2
|
||||
const parentContext = await contextExtractor.getParentContext(noteId);
|
||||
if (depth <= 2) return `${parentContext}\n\n${noteContent}`;
|
||||
|
||||
// Add child context for depth >= 3
|
||||
const childContext = await contextExtractor.getChildContext(noteId);
|
||||
if (depth <= 3) return `${parentContext}\n\n${noteContent}\n\n${childContext}`;
|
||||
|
||||
// Add linked notes for depth >= 4
|
||||
const linkedContext = await contextExtractor.getLinkedNotesContext(noteId);
|
||||
return `${parentContext}\n\n${noteContent}\n\n${childContext}\n\n${linkedContext}`;
|
||||
}
|
||||
|
||||
/**
|
||||
* Get parent notes in the hierarchy
|
||||
* Helper method that queries the database directly
|
||||
*/
|
||||
private async getParentNotes(noteId: string, maxDepth: number): Promise<{noteId: string, title: string}[]> {
|
||||
const parentNotes: {noteId: string, title: string}[] = [];
|
||||
let currentNoteId = noteId;
|
||||
|
||||
for (let i = 0; i < maxDepth; i++) {
|
||||
const parent = await sql.getRow<{parentNoteId: string, title: string}>(
|
||||
`SELECT branches.parentNoteId, notes.title
|
||||
FROM branches
|
||||
JOIN notes ON branches.parentNoteId = notes.noteId
|
||||
WHERE branches.noteId = ? AND branches.isDeleted = 0 LIMIT 1`,
|
||||
[currentNoteId]
|
||||
);
|
||||
|
||||
if (!parent || parent.parentNoteId === 'root') {
|
||||
break;
|
||||
}
|
||||
|
||||
parentNotes.unshift({
|
||||
noteId: parent.parentNoteId,
|
||||
title: parent.title
|
||||
});
|
||||
|
||||
currentNoteId = parent.parentNoteId;
|
||||
}
|
||||
|
||||
return parentNotes;
|
||||
}
|
||||
|
||||
/**
|
||||
* Get child notes
|
||||
* Helper method that queries the database directly
|
||||
*/
|
||||
private async getChildNotes(noteId: string, maxChildren: number): Promise<{noteId: string, title: string}[]> {
|
||||
return await sql.getRows<{noteId: string, title: string}>(
|
||||
`SELECT noteId, title FROM notes
|
||||
WHERE parentNoteId = ? AND isDeleted = 0
|
||||
LIMIT ?`,
|
||||
[noteId, maxChildren]
|
||||
);
|
||||
}
|
||||
|
||||
/**
|
||||
* Get linked notes
|
||||
* Helper method that queries the database directly
|
||||
*/
|
||||
private async getLinkedNotes(noteId: string, maxLinks: number): Promise<{noteId: string, title: string}[]> {
|
||||
return await sql.getRows<{noteId: string, title: string}>(
|
||||
`SELECT noteId, title FROM notes
|
||||
WHERE noteId IN (
|
||||
SELECT value FROM attributes
|
||||
WHERE noteId = ? AND type = 'relation'
|
||||
LIMIT ?
|
||||
)`,
|
||||
[noteId, maxLinks]
|
||||
);
|
||||
}
|
||||
|
||||
/**
|
||||
* Smart context selection that combines semantic matching with progressive loading
|
||||
* Returns the most appropriate context based on the query and available information
|
||||
*
|
||||
* @param noteId - The ID of the note to get context for
|
||||
* @param userQuery - The user's query for semantic relevance matching
|
||||
* @returns The optimal context for answering the query
|
||||
*/
|
||||
async getSmartContext(noteId: string, userQuery: string): Promise<string> {
|
||||
// Check if embedding provider is available
|
||||
const provider = await this.getPreferredEmbeddingProvider();
|
||||
|
||||
if (provider) {
|
||||
try {
|
||||
const semanticContext = await this.getSemanticContext(noteId, userQuery);
|
||||
if (semanticContext) {
|
||||
return semanticContext;
|
||||
}
|
||||
} catch (error) {
|
||||
log.error(`Error getting semantic context: ${error}`);
|
||||
// Fall back to progressive context if semantic fails
|
||||
}
|
||||
}
|
||||
|
||||
// Default to progressive context with appropriate depth based on query complexity
|
||||
// Simple queries get less context, complex ones get more
|
||||
const queryComplexity = this.estimateQueryComplexity(userQuery);
|
||||
const depth = Math.min(4, Math.max(1, queryComplexity));
|
||||
|
||||
return this.getProgressiveContext(noteId, depth);
|
||||
}
|
||||
|
||||
/**
|
||||
* Estimate query complexity to determine appropriate context depth
|
||||
*
|
||||
* @param query - The user's query string
|
||||
* @returns Complexity score from 1-4
|
||||
*/
|
||||
private estimateQueryComplexity(query: string): number {
|
||||
if (!query) return 1;
|
||||
|
||||
// Simple heuristics for query complexity:
|
||||
// 1. Length (longer queries tend to be more complex)
|
||||
// 2. Number of questions or specific requests
|
||||
// 3. Presence of complex terms/concepts
|
||||
|
||||
const words = query.split(/\s+/).length;
|
||||
const questions = (query.match(/\?/g) || []).length;
|
||||
const comparisons = (query.match(/compare|difference|versus|vs\.|between/gi) || []).length;
|
||||
const complexity = (query.match(/explain|analyze|synthesize|evaluate|critique|recommend|suggest/gi) || []).length;
|
||||
|
||||
// Calculate complexity score
|
||||
let score = 1;
|
||||
|
||||
if (words > 20) score += 1;
|
||||
if (questions > 1) score += 1;
|
||||
if (comparisons > 0) score += 1;
|
||||
if (complexity > 0) score += 1;
|
||||
|
||||
return Math.min(4, score);
|
||||
}
|
||||
}
|
||||
|
||||
// Singleton instance
|
||||
const semanticContextService = new SemanticContextService();
|
||||
export default semanticContextService;
|
||||
Loading…
x
Reference in New Issue
Block a user