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adapt or regenerate embeddings - allows users to decide

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perf3ct 2025-03-17 21:47:11 +00:00
parent 5ad730c153
commit 84a8473beb
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7 changed files with 154 additions and 44 deletions
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17
src/public/app/widgets/type_widgets/options/ai_settings.ts
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@ -340,6 +340,15 @@ export default class AiSettingsWidget extends OptionsWidget {
<div class="form-text">${t("ai_llm.embedding_default_provider_description")}</div>
</div>
<div class="form-group">
<label>${t("ai_llm.embedding_dimension_strategy")}</label>
<select class="embedding-dimension-strategy form-control">
<option value="adapt">Adapt dimensions (faster)</option>
<option value="regenerate">Regenerate embeddings (more accurate)</option>
</select>
<div class="form-text">${t("ai_llm.embedding_dimension_strategy_description") || "Choose how to handle different embedding dimensions between providers. 'Adapt' is faster but less accurate, 'Regenerate' is more accurate but requires API calls."}</div>
</div>
<div class="form-group">
<label>${t("ai_llm.embedding_provider_precedence")}</label>
<input type="hidden" class="embedding-provider-precedence" value="">
@ -812,6 +821,11 @@ export default class AiSettingsWidget extends OptionsWidget {
await this.displayValidationWarnings();
});
const $embeddingDimensionStrategy = this.$widget.find('.embedding-dimension-strategy');
$embeddingDimensionStrategy.on('change', async () => {
await this.updateOption('embeddingDimensionStrategy', $embeddingDimensionStrategy.val() as string);
});
const $embeddingProviderPrecedence = this.$widget.find('.embedding-provider-precedence');
$embeddingProviderPrecedence.on('change', async () => {
await this.updateOption('embeddingProviderPrecedence', $embeddingProviderPrecedence.val() as string);
@ -1151,7 +1165,8 @@ export default class AiSettingsWidget extends OptionsWidget {
this.$widget.find('.embedding-similarity-threshold').val(options.embeddingSimilarityThreshold || '0.65');
this.$widget.find('.max-notes-per-llm-query').val(options.maxNotesPerLlmQuery || '10');
this.$widget.find('.embedding-default-provider').val(options.embeddingsDefaultProvider || 'openai');
this.$widget.find('.embedding-provider-precedence').val(options.embeddingProviderPrecedence || 'openai,ollama,anthropic');
this.$widget.find('.embedding-provider-precedence').val(options.embeddingProviderPrecedence || 'openai,ollama');
this.$widget.find('.embedding-dimension-strategy').val(options.embeddingDimensionStrategy || 'adapt');
this.$widget.find('.embedding-generation-location').val(options.embeddingGenerationLocation || 'client');
this.$widget.find('.embedding-batch-size').val(options.embeddingBatchSize || '10');
this.$widget.find('.embedding-update-interval').val(options.embeddingUpdateInterval || '5000');

3
src/routes/api/options.ts
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@ -106,7 +106,8 @@ const ALLOWED_OPTIONS = new Set([
"embeddingSimilarityThreshold",
"maxNotesPerLlmQuery",
"enableAutomaticIndexing",
"embeddingGenerationLocation"
"embeddingGenerationLocation",
"embeddingDimensionStrategy"
]);
function getOptions() {

103
src/services/llm/embeddings/storage.ts
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@ -165,18 +165,25 @@ export async function findSimilarNotes(
log.info(`Available embeddings: ${JSON.stringify(availableEmbeddings.map(e => ({
providerId: e.providerId,
modelId: e.modelId,
count: e.count
count: e.count,
dimension: e.dimension
})))}`);
// Import the AIServiceManager to get provider precedence
const { default: aiManager } = await import('../ai_service_manager.js');
// Import vector utils for dimension adaptation
const { adaptEmbeddingDimensions } = await import('./vector_utils.js');
// Get user dimension strategy preference
const options = (await import('../../options.js')).default;
const dimensionStrategy = await options.getOption('embeddingDimensionStrategy') || 'adapt';
log.info(`Using embedding dimension strategy: ${dimensionStrategy}`);
// Get providers in user-defined precedence order
// This uses the internal providerOrder property that's set from user preferences
const availableProviderIds = availableEmbeddings.map(e => e.providerId);
// Get dedicated embedding provider precedence from options
const options = (await import('../../options.js')).default;
let preferredProviders: string[] = [];
const embeddingPrecedence = await options.getOption('embeddingProviderPrecedence');
@ -215,53 +222,54 @@ export async function findSimilarNotes(
const providerEmbeddings = availableEmbeddings.filter(e => e.providerId === provider);
if (providerEmbeddings.length > 0) {
// Find models that match the current embedding's dimensions
const dimensionMatchingModels = providerEmbeddings.filter(e => e.dimension === embedding.length);
// Use the model with the most embeddings
const bestModel = providerEmbeddings.sort((a, b) => b.count - a.count)[0];
log.info(`Found fallback provider: ${provider}, model: ${bestModel.modelId}, dimension: ${bestModel.dimension}`);
// If we have models with matching dimensions, use the one with most embeddings
if (dimensionMatchingModels.length > 0) {
const bestModel = dimensionMatchingModels.sort((a, b) => b.count - a.count)[0];
log.info(`Found fallback provider with matching dimensions (${embedding.length}): ${provider}, model: ${bestModel.modelId}`);
if (dimensionStrategy === 'adapt') {
// Dimension adaptation strategy (simple truncation/padding)
const adaptedEmbedding = adaptEmbeddingDimensions(embedding, bestModel.dimension);
log.info(`Adapted query embedding from dimension ${embedding.length} to ${adaptedEmbedding.length}`);
// Recursive call with the new provider/model, but disable further fallbacks
// Use the adapted embedding with the fallback provider
return findSimilarNotes(
embedding,
adaptedEmbedding,
provider,
bestModel.modelId,
limit,
threshold,
false // Prevent infinite recursion
);
} else {
// We need to regenerate embeddings with the new provider
log.info(`No models with matching dimensions found for ${provider}. Available models: ${JSON.stringify(
providerEmbeddings.map(e => ({ model: e.modelId, dimension: e.dimension }))
)}`);
}
else if (dimensionStrategy === 'regenerate') {
// Regeneration strategy (regenerate embedding with fallback provider)
try {
// Import provider manager to get a provider instance
const { default: providerManager } = await import('./providers.js');
const providerInstance = providerManager.getEmbeddingProvider(provider);
if (providerInstance) {
// Use the model with the most embeddings
const bestModel = providerEmbeddings.sort((a, b) => b.count - a.count)[0];
// Configure the model by setting it in the config
try {
// Access the config safely through the getConfig method
// Try to get the original query text
// This is a challenge - ideally we would have the original query
// For now, we'll use a global cache to store recent queries
interface CustomGlobal {
recentEmbeddingQueries?: Record<string, string>;
}
const globalWithCache = global as unknown as CustomGlobal;
const recentQueries = globalWithCache.recentEmbeddingQueries || {};
const embeddingKey = embedding.toString().substring(0, 100);
const originalQuery = recentQueries[embeddingKey];
if (originalQuery) {
log.info(`Found original query "${originalQuery}" for regeneration with ${provider}`);
// Configure the model
const config = providerInstance.getConfig();
config.model = bestModel.modelId;
log.info(`Trying to convert query to ${provider}/${bestModel.modelId} embedding format (dimension: ${bestModel.dimension})`);
// Get the original query from the embedding cache if possible, or use a placeholder
// This is a hack - ideally we'd pass the query text through the whole chain
const originalQuery = "query"; // This is a placeholder, we'd need the original query text
// Generate a new embedding with the fallback provider
const newEmbedding = await providerInstance.generateEmbeddings(originalQuery);
log.info(`Successfully generated new embedding with provider ${provider}/${bestModel.modelId} (dimension: ${newEmbedding.length})`);
log.info(`Successfully regenerated embedding with provider ${provider}/${bestModel.modelId} (dimension: ${newEmbedding.length})`);
// Now try finding similar notes with the new embedding
return findSimilarNotes(
@ -272,18 +280,38 @@ export async function findSimilarNotes(
threshold,
false // Prevent infinite recursion
);
} catch (configErr: any) {
log.error(`Error configuring provider ${provider}: ${configErr.message}`);
} else {
log.info(`Original query not found for regeneration, falling back to adaptation`);
// Fall back to adaptation if we can't find the original query
const adaptedEmbedding = adaptEmbeddingDimensions(embedding, bestModel.dimension);
return findSimilarNotes(
adaptedEmbedding,
provider,
bestModel.modelId,
limit,
threshold,
false
);
}
}
} catch (err: any) {
log.error(`Error converting embedding format: ${err.message}`);
log.error(`Error regenerating embedding: ${err.message}`);
// Fall back to adaptation on error
const adaptedEmbedding = adaptEmbeddingDimensions(embedding, bestModel.dimension);
return findSimilarNotes(
adaptedEmbedding,
provider,
bestModel.modelId,
limit,
threshold,
false
);
}
}
}
}
log.error(`No suitable fallback providers found with compatible dimensions. Current embedding dimension: ${embedding.length}`);
log.error(`No suitable fallback providers found. Current embedding dimension: ${embedding.length}`);
log.info(`Available embeddings: ${JSON.stringify(availableEmbeddings.map(e => ({
providerId: e.providerId,
modelId: e.modelId,
@ -307,13 +335,8 @@ export async function findSimilarNotes(
const rowData = row as any;
const rowEmbedding = bufferToEmbedding(rowData.embedding, rowData.dimension);
// Check if dimensions match before calculating similarity
if (rowEmbedding.length !== embedding.length) {
log.info(`Skipping embedding ${rowData.embedId} - dimension mismatch: ${rowEmbedding.length} vs ${embedding.length}`);
continue;
}
try {
// cosineSimilarity will automatically adapt dimensions if needed
const similarity = cosineSimilarity(embedding, rowEmbedding);
similarities.push({
noteId: rowData.noteId,

50
src/services/llm/embeddings/vector_utils.ts
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@ -1,9 +1,11 @@
/**
* Computes the cosine similarity between two vectors
* If dimensions don't match, automatically adapts the first vector to match the second
*/
export function cosineSimilarity(a: Float32Array, b: Float32Array): number {
// If dimensions don't match, adapt 'a' to match 'b'
if (a.length !== b.length) {
throw new Error(`Vector dimensions don't match: ${a.length} vs ${b.length}`);
a = adaptEmbeddingDimensions(a, b.length);
}
let dotProduct = 0;
@ -26,6 +28,52 @@ export function cosineSimilarity(a: Float32Array, b: Float32Array): number {
return dotProduct / (aMagnitude * bMagnitude);
}
/**
* Adapts an embedding to match target dimensions
* Uses a simple truncation (if source is larger) or zero-padding (if source is smaller)
*
* @param sourceEmbedding The original embedding
* @param targetDimension The desired dimension
* @returns A new embedding with the target dimensions
*/
export function adaptEmbeddingDimensions(sourceEmbedding: Float32Array, targetDimension: number): Float32Array {
const sourceDimension = sourceEmbedding.length;
// If dimensions already match, return the original
if (sourceDimension === targetDimension) {
return sourceEmbedding;
}
// Create a new embedding with target dimensions
const adaptedEmbedding = new Float32Array(targetDimension);
if (sourceDimension < targetDimension) {
// If source is smaller, copy all values and pad with zeros
adaptedEmbedding.set(sourceEmbedding);
// Rest of the array is already initialized to zeros
} else {
// If source is larger, truncate to target dimension
for (let i = 0; i < targetDimension; i++) {
adaptedEmbedding[i] = sourceEmbedding[i];
}
}
// Normalize the adapted embedding to maintain unit length
let magnitude = 0;
for (let i = 0; i < targetDimension; i++) {
magnitude += adaptedEmbedding[i] * adaptedEmbedding[i];
}
magnitude = Math.sqrt(magnitude);
if (magnitude > 0) {
for (let i = 0; i < targetDimension; i++) {
adaptedEmbedding[i] /= magnitude;
}
}
return adaptedEmbedding;
}
/**
* Converts embedding Float32Array to Buffer for storage in SQLite
*/

21
src/services/llm/index_service.ts
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@ -543,6 +543,27 @@ class IndexService {
const embedding = await provider.generateEmbeddings(query);
log.info(`Generated embedding for query: "${query}" (${embedding.length} dimensions)`);
// Store query text in a global cache for possible regeneration with different providers
// Use a type declaration to avoid TypeScript errors
interface CustomGlobal {
recentEmbeddingQueries?: Record<string, string>;
}
const globalWithCache = global as unknown as CustomGlobal;
if (!globalWithCache.recentEmbeddingQueries) {
globalWithCache.recentEmbeddingQueries = {};
}
// Use a substring of the embedding as a key (full embedding is too large)
const embeddingKey = embedding.toString().substring(0, 100);
globalWithCache.recentEmbeddingQueries[embeddingKey] = query;
// Limit cache size to prevent memory leaks (keep max 50 recent queries)
const keys = Object.keys(globalWithCache.recentEmbeddingQueries);
if (keys.length > 50) {
delete globalWithCache.recentEmbeddingQueries[keys[0]];
}
// Get Note IDs to search, optionally filtered by branch
let similarNotes = [];

3
src/services/options_init.ts
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@ -189,7 +189,8 @@ const defaultOptions: DefaultOption[] = [
{ name: "aiSystemPrompt", value: "", isSynced: true },
{ name: "aiProviderPrecedence", value: "openai,anthropic,ollama", isSynced: true },
{ name: "embeddingsDefaultProvider", value: "openai", isSynced: true },
{ name: "embeddingProviderPrecedence", value: "openai,ollama,anthropic", isSynced: true },
{ name: "embeddingProviderPrecedence", value: "openai,ollama", isSynced: true },
{ name: "embeddingDimensionStrategy", value: "adapt", isSynced: true },
{ name: "enableAutomaticIndexing", value: "true", isSynced: true },
{ name: "embeddingSimilarityThreshold", value: "0.65", isSynced: true },
{ name: "maxNotesPerLlmQuery", value: "10", isSynced: true },

1
src/services/options_interface.ts
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@ -77,6 +77,7 @@ export interface OptionDefinitions extends KeyboardShortcutsOptions<KeyboardActi
embeddingSimilarityThreshold: string;
maxNotesPerLlmQuery: string;
embeddingGenerationLocation: string;
embeddingDimensionStrategy: string; // 'adapt' or 'regenerate'
lastSyncedPull: number;
lastSyncedPush: number;