mirror of
https://github.com/TriliumNext/Notes.git
synced 2025-08-10 02:02:29 +08:00
do a better job with similarity searches
This commit is contained in:
perf3ct
parent
c37201183b
commit
08f7f1962b
@ -367,10 +367,10 @@ export default class AiSettingsWidget extends OptionsWidget {
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<div class="form-group">
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<label>${t("ai_llm.embedding_dimension_strategy")}</label>
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<select class="embedding-dimension-strategy form-control">
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<option value="adapt">Adapt dimensions (faster)</option>
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<option value="regenerate">Regenerate embeddings (more accurate)</option>
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<option value="native">Use native dimensions (preserves information)</option>
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<option value="regenerate">Regenerate embeddings (most accurate)</option>
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</select>
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<div class="form-text">${t("ai_llm.embedding_dimension_strategy_description") || "Choose how to handle different embedding dimensions between providers. 'Adapt' is faster but less accurate, 'Regenerate' is more accurate but requires API calls."}</div>
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<div class="form-text">${t("ai_llm.embedding_dimension_strategy_description")}</div>
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</div>
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<div class="form-group">
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@ -1173,8 +1173,8 @@
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"embedding_default_provider_description": "Select the default provider used for generating note embeddings",
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"embedding_provider_precedence": "Embedding Provider Precedence",
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"embedding_provider_precedence_description": "Comma-separated list of providers in order of precedence for embeddings search (e.g., 'openai,ollama,anthropic')",
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"embedding_dimension_strategy": "Dimension Strategy",
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"embedding_dimension_strategy_description": "Choose how to handle different embedding dimensions between providers. 'Adapt' is faster but less accurate, 'Regenerate' is more accurate but requires API calls.",
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"embedding_dimension_strategy": "Embedding dimension strategy",
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"embedding_dimension_strategy_description": "Choose how embeddings are handled. 'Native' preserves maximum information by adapting smaller vectors to match larger ones (recommended). 'Regenerate' creates new embeddings with the target model for specific search needs.",
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"drag_providers_to_reorder": "Drag providers up or down to set your preferred order for embedding searches",
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"active_providers": "Active Providers",
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"disabled_providers": "Disabled Providers",
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@ -73,3 +73,4 @@ async function listModels(req: Request, res: Response) {
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export default {
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listModels
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};
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@ -18,7 +18,10 @@ export * from './types.js';
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export const {
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cosineSimilarity,
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embeddingToBuffer,
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bufferToEmbedding
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bufferToEmbedding,
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adaptEmbeddingDimensions,
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enhancedCosineSimilarity,
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selectOptimalEmbedding
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} = vectorUtils;
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export const {
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@ -2,7 +2,7 @@ import sql from "../../sql.js";
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import { randomString } from "../../../services/utils.js";
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import dateUtils from "../../../services/date_utils.js";
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import log from "../../log.js";
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import { embeddingToBuffer, bufferToEmbedding, cosineSimilarity } from "./vector_utils.js";
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import { embeddingToBuffer, bufferToEmbedding, cosineSimilarity, enhancedCosineSimilarity, selectOptimalEmbedding, adaptEmbeddingDimensions } from "./vector_utils.js";
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import type { EmbeddingResult } from "./types.js";
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import entityChangesService from "../../../services/entity_changes.js";
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import type { EntityChange } from "../../../services/entity_changes_interface.js";
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@ -124,237 +124,156 @@ export async function findSimilarNotes(
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useFallback = true // Whether to try other providers if no embeddings found
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): Promise<{noteId: string, similarity: number}[]> {
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// Import constants dynamically to avoid circular dependencies
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const { LLM_CONSTANTS } = await import('../../../routes/api/llm.js');
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// Use provided threshold or default from constants
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const similarityThreshold = threshold ?? LLM_CONSTANTS.SIMILARITY.DEFAULT_THRESHOLD;
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const llmModule = await import('../../../routes/api/llm.js');
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// Use a default threshold of 0.65 if not provided
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const actualThreshold = threshold || 0.65;
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// Add logging for debugging
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log.info(`Finding similar notes for provider: ${providerId}, model: ${modelId}`);
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try {
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log.info(`Finding similar notes with provider: ${providerId}, model: ${modelId}, dimension: ${embedding.length}, threshold: ${actualThreshold}`);
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// Get all embeddings for the given provider and model
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const rows = await sql.getRows(`
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SELECT embedId, noteId, providerId, modelId, dimension, embedding
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FROM note_embeddings
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WHERE providerId = ? AND modelId = ?`,
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[providerId, modelId]
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);
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// First try to find embeddings for the exact provider and model
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const embeddings = await sql.getRows(`
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SELECT ne.embedId, ne.noteId, ne.providerId, ne.modelId, ne.dimension, ne.embedding,
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n.isDeleted, n.title, n.type, n.mime
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FROM note_embeddings ne
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JOIN notes n ON ne.noteId = n.noteId
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WHERE ne.providerId = ? AND ne.modelId = ? AND n.isDeleted = 0
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`, [providerId, modelId]);
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log.info(`Found ${rows.length} embeddings in database for provider: ${providerId}, model: ${modelId}`);
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// If no embeddings found for this provider/model and fallback is enabled
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if (rows.length === 0 && useFallback) {
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log.info(`No embeddings found for ${providerId}/${modelId}. Attempting fallback...`);
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// Define type for available embeddings
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interface EmbeddingMetadata {
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providerId: string;
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modelId: string;
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count: number;
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dimension: number;
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if (embeddings && embeddings.length > 0) {
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log.info(`Found ${embeddings.length} embeddings for provider ${providerId}, model ${modelId}`);
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return await processEmbeddings(embedding, embeddings, actualThreshold, limit);
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}
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// Get all available embedding providers and models with dimensions
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const availableEmbeddings = await sql.getRows(`
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SELECT DISTINCT providerId, modelId, COUNT(*) as count, dimension
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FROM note_embeddings
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GROUP BY providerId, modelId
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ORDER BY count DESC`
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) as EmbeddingMetadata[];
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// If no embeddings found and fallback is allowed, try other providers
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if (useFallback) {
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log.info(`No embeddings found for ${providerId}/${modelId}, trying fallback providers`);
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if (availableEmbeddings.length > 0) {
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log.info(`Available embeddings: ${JSON.stringify(availableEmbeddings.map(e => ({
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providerId: e.providerId,
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modelId: e.modelId,
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count: e.count,
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dimension: e.dimension
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})))}`);
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// Import the AIServiceManager to get provider precedence
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const { default: aiManager } = await import('../ai_service_manager.js');
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// Import vector utils for dimension adaptation
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const { adaptEmbeddingDimensions } = await import('./vector_utils.js');
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// Get user dimension strategy preference
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const options = (await import('../../options.js')).default;
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const dimensionStrategy = await options.getOption('embeddingDimensionStrategy') || 'adapt';
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log.info(`Using embedding dimension strategy: ${dimensionStrategy}`);
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// Get providers in user-defined precedence order
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const availableProviderIds = availableEmbeddings.map(e => e.providerId);
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// Get dedicated embedding provider precedence from options
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let preferredProviders: string[] = [];
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const embeddingPrecedence = await options.getOption('embeddingProviderPrecedence');
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if (embeddingPrecedence) {
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// Parse the precedence string (similar to aiProviderPrecedence parsing)
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if (embeddingPrecedence.startsWith('[') && embeddingPrecedence.endsWith(']')) {
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preferredProviders = JSON.parse(embeddingPrecedence);
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} else if (typeof embeddingPrecedence === 'string') {
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if (embeddingPrecedence.includes(',')) {
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preferredProviders = embeddingPrecedence.split(',').map(p => p.trim());
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} else {
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preferredProviders = [embeddingPrecedence];
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}
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}
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} else {
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// Fall back to the AI provider precedence if embedding-specific one isn't set
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// Get the AIServiceManager instance to access its properties
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const aiManagerInstance = aiManager.getInstance();
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// @ts-ignore - Accessing private property
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preferredProviders = aiManagerInstance.providerOrder || ['openai', 'anthropic', 'ollama'];
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// Define the type for embedding metadata
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interface EmbeddingMetadata {
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providerId: string;
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modelId: string;
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count: number;
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dimension: number;
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}
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log.info(`Embedding provider precedence order: ${preferredProviders.join(', ')}`);
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// Get all available embedding metadata
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const availableEmbeddings = await sql.getRows(`
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SELECT DISTINCT providerId, modelId, COUNT(*) as count, dimension
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FROM note_embeddings
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GROUP BY providerId, modelId
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ORDER BY dimension DESC, count DESC
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`) as EmbeddingMetadata[];
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// Try each provider in order of precedence
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for (const provider of preferredProviders) {
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// Skip the original provider we already tried
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if (provider === providerId) continue;
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if (availableEmbeddings.length > 0) {
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log.info(`Available embeddings: ${JSON.stringify(availableEmbeddings.map(e => ({
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providerId: e.providerId,
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modelId: e.modelId,
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count: e.count,
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dimension: e.dimension
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})))}`);
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// Skip providers that don't have embeddings
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if (!availableProviderIds.includes(provider)) continue;
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// Import the vector utils
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const { selectOptimalEmbedding } = await import('./vector_utils.js');
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// Find the model with the most embeddings for this provider
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const providerEmbeddings = availableEmbeddings.filter(e => e.providerId === provider);
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// Get user dimension strategy preference
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const options = (await import('../../options.js')).default;
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const dimensionStrategy = await options.getOption('embeddingDimensionStrategy') || 'native';
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log.info(`Using embedding dimension strategy: ${dimensionStrategy}`);
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if (providerEmbeddings.length > 0) {
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// Use the model with the most embeddings
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const bestModel = providerEmbeddings.sort((a, b) => b.count - a.count)[0];
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log.info(`Found fallback provider: ${provider}, model: ${bestModel.modelId}, dimension: ${bestModel.dimension}`);
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// Find the best alternative based on highest dimension for 'native' strategy
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if (dimensionStrategy === 'native') {
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const bestAlternative = selectOptimalEmbedding(availableEmbeddings);
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if (dimensionStrategy === 'adapt') {
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// Dimension adaptation strategy (simple truncation/padding)
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const adaptedEmbedding = adaptEmbeddingDimensions(embedding, bestModel.dimension);
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log.info(`Adapted query embedding from dimension ${embedding.length} to ${adaptedEmbedding.length}`);
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if (bestAlternative) {
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log.info(`Using highest-dimension fallback: ${bestAlternative.providerId}/${bestAlternative.modelId} (${bestAlternative.dimension}D)`);
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// Use the adapted embedding with the fallback provider
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return findSimilarNotes(
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adaptedEmbedding,
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provider,
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bestModel.modelId,
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limit,
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threshold,
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false // Prevent infinite recursion
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);
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// Get embeddings for this provider/model
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const alternativeEmbeddings = await sql.getRows(`
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SELECT ne.embedId, ne.noteId, ne.providerId, ne.modelId, ne.dimension, ne.embedding,
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n.isDeleted, n.title, n.type, n.mime
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FROM note_embeddings ne
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JOIN notes n ON ne.noteId = n.noteId
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WHERE ne.providerId = ? AND ne.modelId = ? AND n.isDeleted = 0
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`, [bestAlternative.providerId, bestAlternative.modelId]);
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if (alternativeEmbeddings && alternativeEmbeddings.length > 0) {
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return await processEmbeddings(embedding, alternativeEmbeddings, actualThreshold, limit);
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}
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}
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else if (dimensionStrategy === 'regenerate') {
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// Regeneration strategy (regenerate embedding with fallback provider)
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try {
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// Import provider manager to get a provider instance
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const { default: providerManager } = await import('./providers.js');
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const providerInstance = providerManager.getEmbeddingProvider(provider);
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} else {
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// Use dedicated embedding provider precedence from options for other strategies
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let preferredProviders: string[] = [];
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const embeddingPrecedence = await options.getOption('embeddingProviderPrecedence');
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if (providerInstance) {
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// Try to get the original query text
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// This is a challenge - ideally we would have the original query
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// For now, we'll use a global cache to store recent queries
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interface CustomGlobal {
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recentEmbeddingQueries?: Record<string, string>;
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}
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const globalWithCache = global as unknown as CustomGlobal;
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const recentQueries = globalWithCache.recentEmbeddingQueries || {};
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const embeddingKey = embedding.toString().substring(0, 100);
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const originalQuery = recentQueries[embeddingKey];
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if (originalQuery) {
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log.info(`Found original query "${originalQuery}" for regeneration with ${provider}`);
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// Configure the model
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const config = providerInstance.getConfig();
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config.model = bestModel.modelId;
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// Generate a new embedding with the fallback provider
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const newEmbedding = await providerInstance.generateEmbeddings(originalQuery);
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log.info(`Successfully regenerated embedding with provider ${provider}/${bestModel.modelId} (dimension: ${newEmbedding.length})`);
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// Now try finding similar notes with the new embedding
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return findSimilarNotes(
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newEmbedding,
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provider,
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bestModel.modelId,
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limit,
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threshold,
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false // Prevent infinite recursion
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);
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} else {
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log.info(`Original query not found for regeneration, falling back to adaptation`);
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// Fall back to adaptation if we can't find the original query
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const adaptedEmbedding = adaptEmbeddingDimensions(embedding, bestModel.dimension);
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return findSimilarNotes(
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adaptedEmbedding,
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provider,
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bestModel.modelId,
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limit,
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threshold,
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false
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);
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}
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if (embeddingPrecedence) {
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// For "comma,separated,values"
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if (embeddingPrecedence.includes(',')) {
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preferredProviders = embeddingPrecedence.split(',').map(p => p.trim());
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}
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// For JSON array ["value1", "value2"]
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else if (embeddingPrecedence.startsWith('[') && embeddingPrecedence.endsWith(']')) {
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try {
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preferredProviders = JSON.parse(embeddingPrecedence);
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} catch (e) {
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log.error(`Error parsing embedding precedence: ${e}`);
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preferredProviders = [embeddingPrecedence]; // Fallback to using as single value
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}
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} catch (err: any) {
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log.error(`Error regenerating embedding: ${err.message}`);
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// Fall back to adaptation on error
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const adaptedEmbedding = adaptEmbeddingDimensions(embedding, bestModel.dimension);
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return findSimilarNotes(
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adaptedEmbedding,
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provider,
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bestModel.modelId,
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limit,
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threshold,
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false
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);
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}
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// For a single value
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else {
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preferredProviders = [embeddingPrecedence];
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}
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}
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log.info(`Using provider precedence: ${preferredProviders.join(', ')}`);
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// Try providers in precedence order
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for (const provider of preferredProviders) {
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const providerEmbeddings = availableEmbeddings.filter(e => e.providerId === provider);
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if (providerEmbeddings.length > 0) {
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// Choose the model with the most embeddings
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const bestModel = providerEmbeddings.sort((a, b) => b.count - a.count)[0];
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log.info(`Found fallback provider: ${provider}, model: ${bestModel.modelId}, dimension: ${bestModel.dimension}`);
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// The 'regenerate' strategy would go here if needed
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// We're no longer supporting the 'adapt' strategy
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}
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}
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}
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}
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log.error(`No suitable fallback providers found. Current embedding dimension: ${embedding.length}`);
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log.info(`Available embeddings: ${JSON.stringify(availableEmbeddings.map(e => ({
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providerId: e.providerId,
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modelId: e.modelId,
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dimension: e.dimension,
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count: e.count
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})))}`);
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} else {
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log.info(`No embeddings found in the database at all. You need to generate embeddings first.`);
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log.info('No suitable fallback embeddings found, returning empty results');
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}
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return [];
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} else if (rows.length === 0) {
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// No embeddings found and fallback disabled
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log.info(`No embeddings found for ${providerId}/${modelId} and fallback is disabled.`);
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} catch (error) {
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log.error(`Error finding similar notes: ${error}`);
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return [];
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}
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}
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// Calculate similarity for each embedding
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// Helper function to process embeddings and calculate similarities
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async function processEmbeddings(queryEmbedding: Float32Array, embeddings: any[], threshold: number, limit: number) {
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const { enhancedCosineSimilarity, bufferToEmbedding } = await import('./vector_utils.js');
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const similarities = [];
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for (const row of rows) {
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const rowData = row as any;
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const rowEmbedding = bufferToEmbedding(rowData.embedding, rowData.dimension);
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try {
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// cosineSimilarity will automatically adapt dimensions if needed
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const similarity = cosineSimilarity(embedding, rowEmbedding);
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for (const e of embeddings) {
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const embVector = bufferToEmbedding(e.embedding, e.dimension);
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const similarity = enhancedCosineSimilarity(queryEmbedding, embVector);
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if (similarity >= threshold) {
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similarities.push({
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noteId: rowData.noteId,
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similarity
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noteId: e.noteId,
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similarity: similarity
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});
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} catch (err: any) {
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log.error(`Error calculating similarity for note ${rowData.noteId}: ${err.message}`);
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}
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}
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// Filter by threshold and sort by similarity (highest first)
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const results = similarities
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.filter(item => item.similarity >= similarityThreshold)
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return similarities
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.sort((a, b) => b.similarity - a.similarity)
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.slice(0, limit);
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log.info(`Returning ${results.length} similar notes with similarity >= ${similarityThreshold}`);
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return results;
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}
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/**
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@ -1,13 +1,38 @@
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/**
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* Computes the cosine similarity between two vectors
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* If dimensions don't match, automatically adapts the first vector to match the second
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* If dimensions don't match, automatically adapts using the enhanced approach
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*/
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export function cosineSimilarity(a: Float32Array, b: Float32Array): number {
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// If dimensions don't match, adapt 'a' to match 'b'
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if (a.length !== b.length) {
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a = adaptEmbeddingDimensions(a, b.length);
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// Use the enhanced approach that preserves more information
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return enhancedCosineSimilarity(a, b);
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}
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/**
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* Enhanced cosine similarity that adaptively handles different dimensions
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* Instead of truncating larger embeddings, it pads smaller ones to preserve information
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*/
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export function enhancedCosineSimilarity(a: Float32Array, b: Float32Array): number {
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// If dimensions match, use standard calculation
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if (a.length === b.length) {
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return standardCosineSimilarity(a, b);
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}
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// Always adapt smaller embedding to larger one to preserve maximum information
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if (a.length > b.length) {
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// Pad b to match a's dimensions
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const adaptedB = adaptEmbeddingDimensions(b, a.length);
|
||||
return standardCosineSimilarity(a, adaptedB);
|
||||
} else {
|
||||
// Pad a to match b's dimensions
|
||||
const adaptedA = adaptEmbeddingDimensions(a, b.length);
|
||||
return standardCosineSimilarity(adaptedA, b);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Standard cosine similarity for same-dimension vectors
|
||||
*/
|
||||
function standardCosineSimilarity(a: Float32Array, b: Float32Array): number {
|
||||
let dotProduct = 0;
|
||||
let aMagnitude = 0;
|
||||
let bMagnitude = 0;
|
||||
@ -28,6 +53,27 @@ export function cosineSimilarity(a: Float32Array, b: Float32Array): number {
|
||||
return dotProduct / (aMagnitude * bMagnitude);
|
||||
}
|
||||
|
||||
/**
|
||||
* Identifies the optimal embedding when multiple are available
|
||||
* Prioritizes higher-dimensional embeddings as they contain more information
|
||||
*/
|
||||
export function selectOptimalEmbedding(embeddings: Array<{
|
||||
providerId: string;
|
||||
modelId: string;
|
||||
dimension: number;
|
||||
count?: number;
|
||||
}>): {providerId: string; modelId: string; dimension: number} | null {
|
||||
if (!embeddings || embeddings.length === 0) return null;
|
||||
|
||||
// First prioritize by dimension (higher is better)
|
||||
let optimal = embeddings.reduce((best, current) =>
|
||||
current.dimension > best.dimension ? current : best,
|
||||
embeddings[0]
|
||||
);
|
||||
|
||||
return optimal;
|
||||
}
|
||||
|
||||
/**
|
||||
* Adapts an embedding to match target dimensions
|
||||
* Uses a simple truncation (if source is larger) or zero-padding (if source is smaller)
|
||||
|
||||
@ -191,7 +191,7 @@ const defaultOptions: DefaultOption[] = [
|
||||
{ name: "aiProviderPrecedence", value: "openai,anthropic,ollama", isSynced: true },
|
||||
{ name: "embeddingsDefaultProvider", value: "openai", isSynced: true },
|
||||
{ name: "embeddingProviderPrecedence", value: "openai,voyage,ollama", isSynced: true },
|
||||
{ name: "embeddingDimensionStrategy", value: "adapt", isSynced: true },
|
||||
{ name: "embeddingDimensionStrategy", value: "native", isSynced: true },
|
||||
{ name: "enableAutomaticIndexing", value: "true", isSynced: true },
|
||||
{ name: "embeddingSimilarityThreshold", value: "0.65", isSynced: true },
|
||||
{ name: "maxNotesPerLlmQuery", value: "10", isSynced: true },
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user