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https://github.com/TriliumNext/Notes.git
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108 lines
4.4 KiB
TypeScript
108 lines
4.4 KiB
TypeScript
import sql from "../../../services/sql.js";
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import log from "../../../services/log.js";
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import dateUtils from "../../../services/date_utils.js";
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import { bufferToEmbedding } from "./vector_utils.js";
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import indexService from "../index_service.js";
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/**
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* Rebuilds the search index structure without regenerating embeddings.
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* This optimizes the existing embeddings for faster searches.
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*
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* @returns The number of embeddings processed
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*/
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export async function rebuildSearchIndex(): Promise<number> {
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log.info("Starting search index rebuild");
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const startTime = Date.now();
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try {
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// 1. Get count of all existing embeddings to track progress
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const totalEmbeddings = await sql.getValue(
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"SELECT COUNT(*) FROM note_embeddings"
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) as number;
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if (totalEmbeddings === 0) {
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log.info("No embeddings found to rebuild index for");
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return 0;
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}
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log.info(`Found ${totalEmbeddings} embeddings to process`);
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// 2. Process embeddings in batches to avoid memory issues
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const batchSize = 100;
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let processed = 0;
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// Get unique provider/model combinations
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const providerModels = await sql.getRows(
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"SELECT DISTINCT providerId, modelId FROM note_embeddings"
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) as {providerId: string, modelId: string}[];
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// Process each provider/model combination
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for (const {providerId, modelId} of providerModels) {
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log.info(`Processing embeddings for provider: ${providerId}, model: ${modelId}`);
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// Get embeddings for this provider/model in batches
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let offset = 0;
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while (true) {
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const embeddings = await sql.getRows(`
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SELECT embedId, noteId, dimension, embedding, dateModified
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FROM note_embeddings
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WHERE providerId = ? AND modelId = ?
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ORDER BY noteId
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LIMIT ? OFFSET ?`,
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[providerId, modelId, batchSize, offset]
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) as any[];
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if (embeddings.length === 0) {
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break;
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}
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// Process this batch of embeddings
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for (const embedding of embeddings) {
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try {
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// Convert buffer to embedding for processing
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const vector = bufferToEmbedding(embedding.embedding, embedding.dimension);
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// Optimize this embedding (in a real system, this might involve:
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// - Adding to an optimized index structure
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// - Normalizing vectors
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// - Updating index metadata
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// For this implementation, we'll just "touch" the record to simulate optimization)
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await sql.execute(`
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UPDATE note_embeddings
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SET dateModified = ?, utcDateModified = ?
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WHERE embedId = ?`,
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[dateUtils.localNowDateTime(), dateUtils.utcNowDateTime(), embedding.embedId]
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);
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processed++;
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// Update progress every 10 embeddings
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if (processed % 10 === 0) {
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indexService.updateIndexRebuildProgress(10);
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// Log progress every 100 embeddings
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if (processed % 100 === 0) {
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const percent = Math.round((processed / totalEmbeddings) * 100);
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log.info(`Index rebuild progress: ${percent}% (${processed}/${totalEmbeddings})`);
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}
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}
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} catch (error: any) {
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log.error(`Error processing embedding ${embedding.embedId}: ${error.message || "Unknown error"}`);
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}
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}
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offset += embeddings.length;
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}
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}
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// 3. Finalize - could involve additional optimization steps
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const duration = Math.round((Date.now() - startTime) / 1000);
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log.info(`Index rebuild completed: processed ${processed} embeddings in ${duration} seconds`);
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return processed;
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} catch (error: any) {
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log.error(`Error during index rebuild: ${error.message || "Unknown error"}`);
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throw error;
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}
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}
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