Notes/src/services/llm/index_service.ts

/**
 * LLM Index Service
 *
 * Centralized service for managing knowledge base indexing for LLM features.
 * This service coordinates:
 * - Note embedding generation and management
 * - Smart context retrieval for LLM queries
 * - Progressive indexing of the knowledge base
 * - Optimization of the semantic search capabilities
 */

import log from "../log.js";
import options from "../options.js";
import becca from "../../becca/becca.js";
import vectorStore from "./embeddings/vector_store.js";
import providerManager from "./embeddings/providers.js";
import { ContextExtractor } from "./context/index.js";
import eventService from "../events.js";
import type { NoteEmbeddingContext } from "./embeddings/embeddings_interface.js";
import type { OptionDefinitions } from "../options_interface.js";

class IndexService {
    private initialized = false;
    private indexingInProgress = false;
    private contextExtractor = new ContextExtractor();
    private automaticIndexingInterval?: NodeJS.Timeout;

    // Configuration
    private defaultQueryDepth = 2;
    private maxNotesPerQuery = 10;
    private defaultSimilarityThreshold = 0.65;
    private indexUpdateInterval = 3600000; // 1 hour in milliseconds

    /**
     * Initialize the index service
     */
    async initialize() {
        if (this.initialized) return;

        try {
            const aiEnabled = await options.getOptionBool('aiEnabled');
            if (!aiEnabled) {
                log.info("Index service: AI features disabled, skipping initialization");
                return;
            }

            // Check if embedding system is ready
            const providers = await providerManager.getEnabledEmbeddingProviders();
            if (!providers || providers.length === 0) {
                throw new Error("No embedding providers available");
            }

            // Setup automatic indexing if enabled
            if (await options.getOptionBool('embeddingAutoUpdate')) {
                this.setupAutomaticIndexing();
            }

            // Listen for note changes to update index
            this.setupEventListeners();

            this.initialized = true;
            log.info("Index service initialized successfully");
        } catch (error: any) {
            log.error(`Error initializing index service: ${error.message || "Unknown error"}`);
            throw error;
        }
    }

    /**
     * Setup event listeners for index updates
     */
    private setupEventListeners() {
        // Listen for note content changes
        eventService.subscribe(eventService.NOTE_CONTENT_CHANGE, ({ entity }) => {
            if (entity && entity.noteId) {
                this.queueNoteForIndexing(entity.noteId);
            }
        });

        // Listen for new notes
        eventService.subscribe(eventService.ENTITY_CREATED, ({ entityName, entity }) => {
            if (entityName === "notes" && entity && entity.noteId) {
                this.queueNoteForIndexing(entity.noteId);
            }
        });

        // Listen for note title changes
        eventService.subscribe(eventService.NOTE_TITLE_CHANGED, ({ noteId }) => {
            if (noteId) {
                this.queueNoteForIndexing(noteId);
            }
        });

        // Listen for changes in AI settings
        eventService.subscribe(eventService.ENTITY_CHANGED, ({ entityName, entity }) => {
            if (entityName === "options" && entity && entity.name) {
                if (entity.name.startsWith('ai') || entity.name.startsWith('embedding')) {
                    log.info("AI settings changed, updating index service configuration");
                    this.updateConfiguration();
                }
            }
        });
    }

    /**
     * Set up automatic indexing of notes
     */
    private setupAutomaticIndexing() {
        // Clear existing interval if any
        if (this.automaticIndexingInterval) {
            clearInterval(this.automaticIndexingInterval);
        }

        // Create new interval
        this.automaticIndexingInterval = setInterval(async () => {
            try {
                if (!this.indexingInProgress) {
                    const stats = await vectorStore.getEmbeddingStats();

                    // Only run automatic indexing if we're below 95% completion
                    if (stats.percentComplete < 95) {
                        log.info(`Starting automatic indexing (current completion: ${stats.percentComplete}%)`);
                        await this.runBatchIndexing(50); // Process 50 notes at a time
                    }
                }
            } catch (error: any) {
                log.error(`Error in automatic indexing: ${error.message || "Unknown error"}`);
            }
        }, this.indexUpdateInterval);

        log.info("Automatic indexing scheduled");
    }

    /**
     * Update service configuration from options
     */
    private async updateConfiguration() {
        try {
            // Update indexing interval
            const intervalMs = parseInt(await options.getOption('embeddingUpdateInterval') || '3600000', 10);
            this.indexUpdateInterval = intervalMs;

            // Update automatic indexing setting
            const autoIndexing = await options.getOptionBool('embeddingAutoUpdate');
            if (autoIndexing && !this.automaticIndexingInterval) {
                this.setupAutomaticIndexing();
            } else if (!autoIndexing && this.automaticIndexingInterval) {
                clearInterval(this.automaticIndexingInterval);
                this.automaticIndexingInterval = undefined;
            }

            // Update similarity threshold
            const similarityThreshold = await options.getOption('embeddingSimilarityThreshold');
            this.defaultSimilarityThreshold = parseFloat(similarityThreshold || '0.65');

            // Update max notes per query
            const maxNotesPerQuery = await options.getOption('maxNotesPerLlmQuery');
            this.maxNotesPerQuery = parseInt(maxNotesPerQuery || '10', 10);

            log.info("Index service configuration updated");
        } catch (error: any) {
            log.error(`Error updating index service configuration: ${error.message || "Unknown error"}`);
        }
    }

    /**
     * Queue a note for indexing
     */
    async queueNoteForIndexing(noteId: string, priority = false) {
        if (!this.initialized) {
            await this.initialize();
        }

        try {
            await vectorStore.queueNoteForEmbedding(noteId, 'UPDATE');
            return true;
        } catch (error: any) {
            log.error(`Error queueing note ${noteId} for indexing: ${error.message || "Unknown error"}`);
            return false;
        }
    }

    /**
     * Start full knowledge base indexing
     * @param force - Whether to force reindexing of all notes
     */
    async startFullIndexing(force = false) {
        if (!this.initialized) {
            await this.initialize();
        }

        if (this.indexingInProgress) {
            throw new Error("Indexing already in progress");
        }

        try {
            this.indexingInProgress = true;

            if (force) {
                // Force reindexing of all notes
                await vectorStore.reprocessAllNotes();
                log.info("Forced reindexing of all notes initiated");
            } else {
                // Check current stats
                const stats = await vectorStore.getEmbeddingStats();

                // Only start indexing if we're below 90% completion
                if (stats.percentComplete < 90) {
                    await vectorStore.reprocessAllNotes();
                    log.info("Full indexing initiated");
                } else {
                    log.info(`Skipping full indexing, already at ${stats.percentComplete}% completion`);
                }
            }

            return true;
        } catch (error: any) {
            log.error(`Error starting full indexing: ${error.message || "Unknown error"}`);
            return false;
        } finally {
            this.indexingInProgress = false;
        }
    }

    /**
     * Run a batch indexing job for a limited number of notes
     * @param batchSize - Maximum number of notes to process
     */
    async runBatchIndexing(batchSize = 20) {
        if (!this.initialized) {
            await this.initialize();
        }

        if (this.indexingInProgress) {
            return false;
        }

        try {
            this.indexingInProgress = true;

            // Process the embedding queue
            await vectorStore.processEmbeddingQueue();

            return true;
        } catch (error: any) {
            log.error(`Error in batch indexing: ${error.message || "Unknown error"}`);
            return false;
        } finally {
            this.indexingInProgress = false;
        }
    }

    /**
     * Get the current indexing statistics
     */
    async getIndexingStats() {
        if (!this.initialized) {
            await this.initialize();
        }

        try {
            const stats = await vectorStore.getEmbeddingStats();

            return {
                ...stats,
                isIndexing: this.indexingInProgress,
                automaticIndexingEnabled: !!this.automaticIndexingInterval
            };
        } catch (error: any) {
            log.error(`Error getting indexing stats: ${error.message || "Unknown error"}`);
            return {
                totalNotesCount: 0,
                embeddedNotesCount: 0,
                queuedNotesCount: 0,
                failedNotesCount: 0,
                percentComplete: 0,
                isIndexing: this.indexingInProgress,
                automaticIndexingEnabled: !!this.automaticIndexingInterval,
                error: error.message || "Unknown error"
            };
        }
    }

    /**
     * Get information about failed embedding attempts
     */
    async getFailedIndexes(limit = 100) {
        if (!this.initialized) {
            await this.initialize();
        }

        try {
            return await vectorStore.getFailedEmbeddingNotes(limit);
        } catch (error: any) {
            log.error(`Error getting failed indexes: ${error.message || "Unknown error"}`);
            return [];
        }
    }

    /**
     * Retry indexing a specific note that previously failed
     */
    async retryFailedNote(noteId: string) {
        if (!this.initialized) {
            await this.initialize();
        }

        try {
            return await vectorStore.retryFailedEmbedding(noteId);
        } catch (error: any) {
            log.error(`Error retrying failed note ${noteId}: ${error.message || "Unknown error"}`);
            return false;
        }
    }

    /**
     * Retry all failed indexing operations
     */
    async retryAllFailedNotes() {
        if (!this.initialized) {
            await this.initialize();
        }

        try {
            const count = await vectorStore.retryAllFailedEmbeddings();
            log.info(`Queued ${count} failed notes for retry`);
            return count;
        } catch (error: any) {
            log.error(`Error retrying all failed notes: ${error.message || "Unknown error"}`);
            return 0;
        }
    }

    /**
     * Find semantically similar notes to a given query
     * @param query - Text query to find similar notes for
     * @param contextNoteId - Optional note ID to restrict search to a branch
     * @param limit - Maximum number of results to return
     */
    async findSimilarNotes(
        query: string,
        contextNoteId?: string,
        limit = 10
    ) {
        if (!this.initialized) {
            await this.initialize();
        }

        try {
            const providers = await providerManager.getEnabledEmbeddingProviders();
            if (!providers || providers.length === 0) {
                throw new Error("No embedding providers available");
            }

            // Use the first enabled provider
            const provider = providers[0];

            // Generate embedding for the query
            const embedding = await provider.generateEmbeddings(query);

            // Check if we need to restrict search to a specific branch
            if (contextNoteId) {
                const note = becca.getNote(contextNoteId);
                if (!note) {
                    throw new Error(`Context note ${contextNoteId} not found`);
                }

                // Get all note IDs in the branch
                const branchNoteIds = new Set<string>();
                const collectNoteIds = (noteId: string) => {
                    branchNoteIds.add(noteId);
                    const note = becca.getNote(noteId);
                    if (note) {
                        for (const childNote of note.getChildNotes()) {
                            if (!branchNoteIds.has(childNote.noteId)) {
                                collectNoteIds(childNote.noteId);
                            }
                        }
                    }
                };

                collectNoteIds(contextNoteId);

                // Get embeddings for all notes in the branch
                const similarNotes = [];
                const config = provider.getConfig();

                for (const noteId of branchNoteIds) {
                    const noteEmbedding = await vectorStore.getEmbeddingForNote(
                        noteId,
                        provider.name,
                        config.model
                    );

                    if (noteEmbedding) {
                        const similarity = vectorStore.cosineSimilarity(embedding, noteEmbedding.embedding);
                        if (similarity >= this.defaultSimilarityThreshold) {
                            const note = becca.getNote(noteId);
                            if (note) {
                                similarNotes.push({
                                    noteId,
                                    title: note.title,
                                    similarity
                                });
                            }
                        }
                    }
                }

                // Sort by similarity and return top results
                return similarNotes
                    .sort((a, b) => b.similarity - a.similarity)
                    .slice(0, limit);
            } else {
                // Search across all notes
                const config = provider.getConfig();
                const similarNotes = await vectorStore.findSimilarNotes(
                    embedding,
                    provider.name,
                    config.model,
                    limit,
                    this.defaultSimilarityThreshold
                );

                // Enhance results with note titles
                return similarNotes.map(result => {
                    const note = becca.getNote(result.noteId);
                    return {
                        noteId: result.noteId,
                        title: note ? note.title : 'Unknown Note',
                        similarity: result.similarity
                    };
                });
            }
        } catch (error: any) {
            log.error(`Error finding similar notes: ${error.message || "Unknown error"}`);
            return [];
        }
    }

    /**
     * Generate context for an LLM query based on relevance to the user's question
     * @param query - The user's question
     * @param contextNoteId - Optional ID of a note to use as context root
     * @param depth - Depth of context to include (1-4)
     */
    async generateQueryContext(
        query: string,
        contextNoteId?: string,
        depth = 2
    ) {
        if (!this.initialized) {
            await this.initialize();
        }

        try {
            // Find similar notes to the query
            const similarNotes = await this.findSimilarNotes(
                query,
                contextNoteId,
                this.maxNotesPerQuery
            );

            if (similarNotes.length === 0) {
                return "I'm an AI assistant helping with your Trilium notes. I couldn't find specific notes related to your query, but I'll try to assist based on general knowledge.";
            }

            // Build context from the similar notes
            let context = `I found some relevant information in your notes that may help answer: "${query}"\n\n`;

            for (const note of similarNotes) {
                const noteObj = becca.getNote(note.noteId);
                if (!noteObj) continue;

                context += `## ${noteObj.title}\n`;

                // Add parent context for better understanding
                const parents = noteObj.getParentNotes();
                if (parents.length > 0) {
                    context += `Path: ${parents.map(p => p.title).join(' > ')}\n`;
                }

                // Add content based on depth
                if (depth >= 2) {
                    const content = await this.contextExtractor.getNoteContent(note.noteId);
                    if (content) {
                        // For larger content, use summary
                        if (content.length > 2000) {
                            const summary = await this.contextExtractor.summarizeContent(content, noteObj.title);
                            context += `${summary}\n[Content summarized due to length]\n\n`;
                        } else {
                            context += `${content}\n\n`;
                        }
                    }
                }

                // Add child note titles for more context if depth >= 3
                if (depth >= 3) {
                    const childNotes = noteObj.getChildNotes();
                    if (childNotes.length > 0) {
                        context += `Child notes: ${childNotes.slice(0, 5).map(n => n.title).join(', ')}`;
                        if (childNotes.length > 5) {
                            context += ` and ${childNotes.length - 5} more`;
                        }
                        context += `\n\n`;
                    }
                }

                // Add attribute context for even deeper understanding if depth >= 4
                if (depth >= 4) {
                    const attributes = noteObj.getOwnedAttributes();
                    if (attributes.length > 0) {
                        const relevantAttrs = attributes.filter(a =>
                            !a.name.startsWith('_') && !a.name.startsWith('child:') && !a.name.startsWith('relation:')
                        );

                        if (relevantAttrs.length > 0) {
                            context += `Attributes: ${relevantAttrs.map(a =>
                                `${a.type === 'label' ? '#' : '~'}${a.name}${a.value ? '=' + a.value : ''}`
                            ).join(', ')}\n\n`;
                        }
                    }
                }
            }

            // Add instructions about how to reference the notes
            context += "When referring to information from these notes in your response, please cite them by their titles " +
                      "(e.g., \"According to your note on [Title]...\"). If the information doesn't contain what you need, " +
                      "just say so and use your general knowledge instead.";

            return context;
        } catch (error: any) {
            log.error(`Error generating query context: ${error.message || "Unknown error"}`);
            return "I'm an AI assistant helping with your Trilium notes. I encountered an error while retrieving context from your notes, but I'll try to assist based on general knowledge.";
        }
    }

    /**
     * Generate a comprehensive index entry for a note
     * This prepares all metadata and contexts for optimal LLM retrieval
     */
    async generateNoteIndex(noteId: string) {
        if (!this.initialized) {
            await this.initialize();
        }

        try {
            const note = becca.getNote(noteId);
            if (!note) {
                throw new Error(`Note ${noteId} not found`);
            }

            // Get complete note context for indexing
            const context = await vectorStore.getNoteEmbeddingContext(noteId);

            // Queue note for embedding with all available providers
            const providers = await providerManager.getEnabledEmbeddingProviders();
            for (const provider of providers) {
                try {
                    const embedding = await provider.generateNoteEmbeddings(context);
                    if (embedding) {
                        const config = provider.getConfig();
                        await vectorStore.storeNoteEmbedding(
                            noteId,
                            provider.name,
                            config.model,
                            embedding
                        );
                    }
                } catch (error: any) {
                    log.error(`Error generating embedding with provider ${provider.name} for note ${noteId}: ${error.message || "Unknown error"}`);
                }
            }

            return true;
        } catch (error: any) {
            log.error(`Error generating note index for ${noteId}: ${error.message || "Unknown error"}`);
            return false;
        }
    }
}

// Create singleton instance
const indexService = new IndexService();
export default indexService;