mirror of
https://github.com/TriliumNext/Notes.git
synced 2025-09-02 21:42:15 +08:00
set up more reasonable context window and dimension sizes
This commit is contained in:
perf3ct
parent
572a03a3f7
commit
697d348286
@ -27,3 +27,4 @@ INSERT INTO options (name, value, isSynced, utcDateModified) VALUES ('embeddings
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INSERT INTO options (name, value, isSynced, utcDateModified) VALUES ('enableAutomaticIndexing', 'true', 1, strftime('%Y-%m-%dT%H:%M:%fZ', 'now'));
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INSERT INTO options (name, value, isSynced, utcDateModified) VALUES ('embeddingSimilarityThreshold', '0.65', 1, strftime('%Y-%m-%dT%H:%M:%fZ', 'now'));
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INSERT INTO options (name, value, isSynced, utcDateModified) VALUES ('maxNotesPerLlmQuery', '10', 1, strftime('%Y-%m-%dT%H:%M:%fZ', 'now'));
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INSERT INTO options (name, value, isSynced, utcDateModified) VALUES ('embeddingBatchSize', '10', 1, strftime('%Y-%m-%dT%H:%M:%fZ', 'now'));
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@ -41,6 +41,37 @@ export const LLM_CONSTANTS = {
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}
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},
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// Model-specific embedding dimensions for Ollama models
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OLLAMA_MODEL_DIMENSIONS: {
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"llama3": 4096,
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"llama3.1": 4096,
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"mistral": 4096,
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"nomic": 768,
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"mxbai": 1024,
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"nomic-embed-text": 768,
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"mxbai-embed-large": 1024,
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"default": 384
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},
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// Model-specific context windows for Ollama models
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OLLAMA_MODEL_CONTEXT_WINDOWS: {
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"llama3": 8192,
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"mistral": 8192,
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"nomic": 32768,
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"mxbai": 32768,
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"nomic-embed-text": 32768,
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"mxbai-embed-large": 32768,
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"default": 4096
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},
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// Batch size configuration
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BATCH_SIZE: {
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OPENAI: 10, // OpenAI can handle larger batches efficiently
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ANTHROPIC: 5, // More conservative for Anthropic
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OLLAMA: 1, // Ollama processes one at a time
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DEFAULT: 5 // Conservative default
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},
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// Chunking parameters
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CHUNKING: {
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DEFAULT_SIZE: 1500,
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@ -1,22 +1,212 @@
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import type { EmbeddingProvider, EmbeddingConfig, NoteEmbeddingContext } from './embeddings_interface.js';
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import log from "../../log.js";
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import { LLM_CONSTANTS } from "../../../routes/api/llm.js";
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import options from "../../options.js";
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/**
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* Base class that implements common functionality for embedding providers
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*/
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export abstract class BaseEmbeddingProvider implements EmbeddingProvider {
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abstract name: string;
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name: string = "base";
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protected config: EmbeddingConfig;
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protected apiKey?: string;
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protected baseUrl: string;
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protected modelInfoCache = new Map<string, any>();
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constructor(config: EmbeddingConfig) {
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this.config = config;
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this.apiKey = config.apiKey;
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this.baseUrl = config.baseUrl || "";
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}
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getConfig(): EmbeddingConfig {
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return this.config;
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return { ...this.config };
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}
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getDimension(): number {
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return this.config.dimension;
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}
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async initialize(): Promise<void> {
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// Default implementation does nothing
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return;
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}
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/**
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* Generate embeddings for a single text
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*/
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abstract generateEmbeddings(text: string): Promise<Float32Array>;
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abstract generateBatchEmbeddings(texts: string[]): Promise<Float32Array[]>;
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/**
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* Get the appropriate batch size for this provider
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* Override in provider implementations if needed
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*/
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protected async getBatchSize(): Promise<number> {
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// Try to get the user-configured batch size
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let configuredBatchSize: number | null = null;
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try {
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const batchSizeStr = await options.getOption('embeddingBatchSize');
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if (batchSizeStr) {
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configuredBatchSize = parseInt(batchSizeStr, 10);
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}
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} catch (error) {
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log.error(`Error getting batch size from options: ${error}`);
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}
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// If user has configured a specific batch size, use that
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if (configuredBatchSize && !isNaN(configuredBatchSize) && configuredBatchSize > 0) {
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return configuredBatchSize;
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}
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// Otherwise use the provider-specific default from constants
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return this.config.batchSize ||
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LLM_CONSTANTS.BATCH_SIZE[this.name.toUpperCase() as keyof typeof LLM_CONSTANTS.BATCH_SIZE] ||
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LLM_CONSTANTS.BATCH_SIZE.DEFAULT;
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}
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/**
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* Process a batch of texts with adaptive handling
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* This method will try to process the batch and reduce batch size if encountering errors
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*/
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protected async processWithAdaptiveBatch<T>(
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items: T[],
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processFn: (batch: T[]) => Promise<any[]>,
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isBatchSizeError: (error: any) => boolean
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): Promise<any[]> {
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const results: any[] = [];
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const failures: { index: number, error: string }[] = [];
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let currentBatchSize = await this.getBatchSize();
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let lastError: Error | null = null;
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// Process items in batches
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for (let i = 0; i < items.length;) {
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const batch = items.slice(i, i + currentBatchSize);
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try {
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// Process the current batch
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const batchResults = await processFn(batch);
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results.push(...batchResults);
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i += batch.length;
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}
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catch (error: any) {
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lastError = error;
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const errorMessage = error.message || 'Unknown error';
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// Check if this is a batch size related error
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if (isBatchSizeError(error) && currentBatchSize > 1) {
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// Reduce batch size and retry
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const newBatchSize = Math.max(1, Math.floor(currentBatchSize / 2));
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console.warn(`Batch size error detected, reducing batch size from ${currentBatchSize} to ${newBatchSize}: ${errorMessage}`);
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currentBatchSize = newBatchSize;
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}
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else if (currentBatchSize === 1) {
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// If we're already at batch size 1, we can't reduce further, so log the error and skip this item
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log.error(`Error processing item at index ${i} with batch size 1: ${errorMessage}`);
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failures.push({ index: i, error: errorMessage });
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i++; // Move to the next item
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}
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else {
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// For other errors, retry with a smaller batch size as a precaution
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const newBatchSize = Math.max(1, Math.floor(currentBatchSize / 2));
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console.warn(`Error processing batch, reducing batch size from ${currentBatchSize} to ${newBatchSize} as a precaution: ${errorMessage}`);
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currentBatchSize = newBatchSize;
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}
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}
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}
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// If all items failed and we have a last error, throw it
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if (results.length === 0 && failures.length > 0 && lastError) {
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throw lastError;
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}
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// If some items failed but others succeeded, log the summary
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if (failures.length > 0) {
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console.warn(`Processed ${results.length} items successfully, but ${failures.length} items failed`);
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}
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return results;
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}
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/**
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* Detect if an error is related to batch size limits
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* Override in provider-specific implementations
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*/
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protected isBatchSizeError(error: any): boolean {
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const errorMessage = error?.message || '';
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const batchSizeErrorPatterns = [
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'batch size', 'too many items', 'too many inputs',
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'input too large', 'payload too large', 'context length',
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'token limit', 'rate limit', 'request too large'
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];
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return batchSizeErrorPatterns.some(pattern =>
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errorMessage.toLowerCase().includes(pattern.toLowerCase())
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);
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}
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/**
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* Generate embeddings for multiple texts
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* Default implementation processes texts one by one
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*/
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async generateBatchEmbeddings(texts: string[]): Promise<Float32Array[]> {
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if (texts.length === 0) {
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return [];
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}
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try {
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return await this.processWithAdaptiveBatch(
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texts,
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async (batch) => {
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const batchResults = await Promise.all(
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batch.map(text => this.generateEmbeddings(text))
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);
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return batchResults;
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},
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this.isBatchSizeError
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);
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}
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catch (error: any) {
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const errorMessage = error.message || "Unknown error";
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log.error(`Batch embedding error for provider ${this.name}: ${errorMessage}`);
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throw new Error(`${this.name} batch embedding error: ${errorMessage}`);
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}
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}
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/**
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* Generate embeddings for a note with its context
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*/
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async generateNoteEmbeddings(context: NoteEmbeddingContext): Promise<Float32Array> {
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const text = [context.title || "", context.content || ""].filter(Boolean).join(" ");
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return this.generateEmbeddings(text);
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}
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/**
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* Generate embeddings for multiple notes with their contexts
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*/
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async generateBatchNoteEmbeddings(contexts: NoteEmbeddingContext[]): Promise<Float32Array[]> {
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if (contexts.length === 0) {
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return [];
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}
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try {
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return await this.processWithAdaptiveBatch(
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contexts,
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async (batch) => {
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const batchResults = await Promise.all(
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batch.map(context => this.generateNoteEmbeddings(context))
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);
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return batchResults;
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},
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this.isBatchSizeError
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);
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}
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catch (error: any) {
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const errorMessage = error.message || "Unknown error";
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log.error(`Batch note embedding error for provider ${this.name}: ${errorMessage}`);
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throw new Error(`${this.name} batch note embedding error: ${errorMessage}`);
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}
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}
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/**
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* Cleans and normalizes text for embeddings by removing excessive whitespace
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@ -157,20 +347,4 @@ export abstract class BaseEmbeddingProvider implements EmbeddingProvider {
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return result;
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}
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/**
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* Default implementation that converts note context to text and generates embeddings
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*/
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async generateNoteEmbeddings(context: NoteEmbeddingContext): Promise<Float32Array> {
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const text = this.generateNoteContextText(context);
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return this.generateEmbeddings(text);
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}
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/**
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* Default implementation that processes notes in batch
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*/
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async generateBatchNoteEmbeddings(contexts: NoteEmbeddingContext[]): Promise<Float32Array[]> {
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const texts = contexts.map(ctx => this.generateNoteContextText(ctx));
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return this.generateBatchEmbeddings(texts);
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}
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}
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@ -36,6 +36,14 @@ export interface NoteEmbeddingContext {
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templateTitles?: string[];
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}
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/**
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* Information about an embedding model's capabilities
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*/
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export interface EmbeddingModelInfo {
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dimension: number;
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contextWindow: number;
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}
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/**
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* Configuration for how embeddings should be generated
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*/
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@ -46,6 +54,8 @@ export interface EmbeddingConfig {
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normalize?: boolean;
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batchSize?: number;
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contextWindowSize?: number;
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apiKey?: string;
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baseUrl?: string;
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}
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/**
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@ -1,25 +1,117 @@
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import { BaseEmbeddingProvider } from "../base_embeddings.js";
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import type { EmbeddingConfig } from "../embeddings_interface.js";
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import axios from "axios";
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import log from "../../../log.js";
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import { BaseEmbeddingProvider } from "../base_embeddings.js";
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import type { EmbeddingConfig, EmbeddingModelInfo } from "../embeddings_interface.js";
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import { LLM_CONSTANTS } from "../../../../routes/api/llm.js";
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interface AnthropicEmbeddingConfig extends EmbeddingConfig {
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apiKey: string;
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baseUrl: string;
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}
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// Anthropic model context window sizes - as of current API version
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const ANTHROPIC_MODEL_CONTEXT_WINDOWS: Record<string, number> = {
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"claude-3-opus-20240229": 200000,
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"claude-3-sonnet-20240229": 180000,
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"claude-3-haiku-20240307": 48000,
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"claude-2.1": 200000,
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"claude-2.0": 100000,
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"claude-instant-1.2": 100000,
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"default": 100000
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};
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/**
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* Anthropic (Claude) embedding provider implementation
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* Anthropic embedding provider implementation
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*/
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export class AnthropicEmbeddingProvider extends BaseEmbeddingProvider {
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name = "anthropic";
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private apiKey: string;
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private baseUrl: string;
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constructor(config: AnthropicEmbeddingConfig) {
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constructor(config: EmbeddingConfig) {
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super(config);
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this.apiKey = config.apiKey;
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this.baseUrl = config.baseUrl;
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}
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/**
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* Initialize the provider by detecting model capabilities
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*/
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async initialize(): Promise<void> {
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const modelName = this.config.model || "claude-3-haiku-20240307";
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try {
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// Detect model capabilities
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const modelInfo = await this.getModelInfo(modelName);
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// Update the config dimension
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this.config.dimension = modelInfo.dimension;
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log.info(`Anthropic model ${modelName} initialized with dimension ${this.config.dimension} and context window ${modelInfo.contextWindow}`);
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} catch (error: any) {
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log.error(`Error initializing Anthropic provider: ${error.message}`);
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}
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}
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/**
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* Try to determine Anthropic model capabilities
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* Note: Anthropic doesn't have a public endpoint for model metadata, so we use a combination
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* of known values and detection by test embeddings
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*/
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private async fetchModelCapabilities(modelName: string): Promise<EmbeddingModelInfo | null> {
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// Anthropic doesn't have a model info endpoint, but we can look up known context sizes
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// and detect embedding dimensions by making a test request
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try {
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// Get context window size from our local registry of known models
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const modelBase = Object.keys(ANTHROPIC_MODEL_CONTEXT_WINDOWS).find(
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model => modelName.startsWith(model)
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) || "default";
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const contextWindow = ANTHROPIC_MODEL_CONTEXT_WINDOWS[modelBase];
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// For embedding dimension, we'll return null and let getModelInfo detect it
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return {
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dimension: 0, // Will be detected by test embedding
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contextWindow
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};
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} catch (error) {
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log.info(`Could not determine capabilities for Anthropic model ${modelName}: ${error}`);
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return null;
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}
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}
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/**
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* Get model information including embedding dimensions
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*/
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async getModelInfo(modelName: string): Promise<EmbeddingModelInfo> {
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// Check cache first
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if (this.modelInfoCache.has(modelName)) {
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return this.modelInfoCache.get(modelName);
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}
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// Try to determine model capabilities
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const capabilities = await this.fetchModelCapabilities(modelName);
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const contextWindow = capabilities?.contextWindow || LLM_CONSTANTS.CONTEXT_WINDOW.ANTHROPIC;
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// For Anthropic, we need to detect embedding dimension with a test call
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try {
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// Detect dimension with a test embedding
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const testEmbedding = await this.generateEmbeddings("Test");
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const dimension = testEmbedding.length;
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const modelInfo: EmbeddingModelInfo = {
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dimension,
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contextWindow
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};
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this.modelInfoCache.set(modelName, modelInfo);
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this.config.dimension = dimension;
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log.info(`Detected Anthropic model ${modelName} with dimension ${dimension} (context: ${contextWindow})`);
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return modelInfo;
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} catch (error: any) {
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// If detection fails, use defaults
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const dimension = LLM_CONSTANTS.EMBEDDING_DIMENSIONS.ANTHROPIC.DEFAULT;
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log.info(`Using default parameters for Anthropic model ${modelName}: dimension ${dimension}, context ${contextWindow}`);
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const modelInfo: EmbeddingModelInfo = { dimension, contextWindow };
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this.modelInfoCache.set(modelName, modelInfo);
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this.config.dimension = dimension;
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return modelInfo;
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}
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}
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/**
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@ -27,11 +119,23 @@ export class AnthropicEmbeddingProvider extends BaseEmbeddingProvider {
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*/
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async generateEmbeddings(text: string): Promise<Float32Array> {
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try {
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if (!text.trim()) {
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return new Float32Array(this.config.dimension);
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}
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// Get model info to check context window
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const modelName = this.config.model || "claude-3-haiku-20240307";
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const modelInfo = await this.getModelInfo(modelName);
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// Trim text if it might exceed context window (rough character estimate)
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const charLimit = modelInfo.contextWindow * 4; // Rough estimate: avg 4 chars per token
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const trimmedText = text.length > charLimit ? text.substring(0, charLimit) : text;
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const response = await axios.post(
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`${this.baseUrl}/embeddings`,
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{
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model: this.config.model || "claude-3-haiku-20240307",
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input: text,
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model: modelName,
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input: trimmedText,
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encoding_format: "float"
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},
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{
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@ -44,8 +148,7 @@ export class AnthropicEmbeddingProvider extends BaseEmbeddingProvider {
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);
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if (response.data && response.data.embedding) {
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const embedding = response.data.embedding;
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return new Float32Array(embedding);
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return new Float32Array(response.data.embedding);
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} else {
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throw new Error("Unexpected response structure from Anthropic API");
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}
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@ -56,23 +159,60 @@ export class AnthropicEmbeddingProvider extends BaseEmbeddingProvider {
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}
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}
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/**
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* More specific implementation of batch size error detection for Anthropic
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*/
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protected isBatchSizeError(error: any): boolean {
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const errorMessage = error?.message || error?.response?.data?.error?.message || '';
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const anthropicBatchSizeErrorPatterns = [
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'batch size', 'too many inputs', 'context length exceeded',
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'token limit', 'rate limit', 'limit exceeded',
|
||||
'too long', 'request too large', 'content too large'
|
||||
];
|
||||
|
||||
return anthropicBatchSizeErrorPatterns.some(pattern =>
|
||||
errorMessage.toLowerCase().includes(pattern.toLowerCase())
|
||||
);
|
||||
}
|
||||
|
||||
/**
|
||||
* Generate embeddings for multiple texts in a single batch
|
||||
*
|
||||
* Note: Anthropic doesn't currently support batch embedding, so we process each text individually
|
||||
* but using the adaptive batch processor to handle errors and retries
|
||||
*/
|
||||
async generateBatchEmbeddings(texts: string[]): Promise<Float32Array[]> {
|
||||
if (texts.length === 0) {
|
||||
return [];
|
||||
}
|
||||
|
||||
try {
|
||||
return await this.processWithAdaptiveBatch(
|
||||
texts,
|
||||
async (batch) => {
|
||||
const results: Float32Array[] = [];
|
||||
|
||||
for (const text of texts) {
|
||||
// For Anthropic, we have to process one at a time
|
||||
for (const text of batch) {
|
||||
// Skip empty texts
|
||||
if (!text.trim()) {
|
||||
results.push(new Float32Array(this.config.dimension));
|
||||
continue;
|
||||
}
|
||||
|
||||
const embedding = await this.generateEmbeddings(text);
|
||||
results.push(embedding);
|
||||
}
|
||||
|
||||
return results;
|
||||
},
|
||||
this.isBatchSizeError
|
||||
);
|
||||
}
|
||||
catch (error: any) {
|
||||
const errorMessage = error.message || "Unknown error";
|
||||
log.error(`Anthropic batch embedding error: ${errorMessage}`);
|
||||
throw new Error(`Anthropic batch embedding error: ${errorMessage}`);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@ -1,30 +1,17 @@
|
||||
import { BaseEmbeddingProvider } from "../base_embeddings.js";
|
||||
import type { EmbeddingConfig } from "../embeddings_interface.js";
|
||||
import axios from "axios";
|
||||
import log from "../../../log.js";
|
||||
|
||||
interface OllamaEmbeddingConfig extends EmbeddingConfig {
|
||||
baseUrl: string;
|
||||
}
|
||||
|
||||
// Model-specific embedding dimensions
|
||||
interface EmbeddingModelInfo {
|
||||
dimension: number;
|
||||
contextWindow: number;
|
||||
}
|
||||
import { BaseEmbeddingProvider } from "../base_embeddings.js";
|
||||
import type { EmbeddingConfig, EmbeddingModelInfo } from "../embeddings_interface.js";
|
||||
import { LLM_CONSTANTS } from "../../../../routes/api/llm.js";
|
||||
|
||||
/**
|
||||
* Ollama embedding provider implementation
|
||||
*/
|
||||
export class OllamaEmbeddingProvider extends BaseEmbeddingProvider {
|
||||
name = "ollama";
|
||||
private baseUrl: string;
|
||||
// Cache for model dimensions to avoid repeated API calls
|
||||
private modelInfoCache = new Map<string, EmbeddingModelInfo>();
|
||||
|
||||
constructor(config: OllamaEmbeddingConfig) {
|
||||
constructor(config: EmbeddingConfig) {
|
||||
super(config);
|
||||
this.baseUrl = config.baseUrl;
|
||||
}
|
||||
|
||||
/**
|
||||
@ -33,43 +20,129 @@ export class OllamaEmbeddingProvider extends BaseEmbeddingProvider {
|
||||
async initialize(): Promise<void> {
|
||||
const modelName = this.config.model || "llama3";
|
||||
try {
|
||||
await this.getModelInfo(modelName);
|
||||
log.info(`Ollama embedding provider initialized with model ${modelName}`);
|
||||
// Detect model capabilities
|
||||
const modelInfo = await this.getModelInfo(modelName);
|
||||
|
||||
// Update the config dimension
|
||||
this.config.dimension = modelInfo.dimension;
|
||||
|
||||
log.info(`Ollama model ${modelName} initialized with dimension ${this.config.dimension} and context window ${modelInfo.contextWindow}`);
|
||||
} catch (error: any) {
|
||||
log.error(`Failed to initialize Ollama embedding provider: ${error.message}`);
|
||||
// Still continue with default dimensions
|
||||
log.error(`Error initializing Ollama provider: ${error.message}`);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Get model information including embedding dimensions
|
||||
* Fetch detailed model information from Ollama API
|
||||
* @param modelName The name of the model to fetch information for
|
||||
*/
|
||||
private async fetchModelCapabilities(modelName: string): Promise<EmbeddingModelInfo | null> {
|
||||
try {
|
||||
// First try the /api/show endpoint which has detailed model information
|
||||
const showResponse = await axios.get(
|
||||
`${this.baseUrl}/api/show`,
|
||||
{
|
||||
params: { name: modelName },
|
||||
headers: { "Content-Type": "application/json" },
|
||||
timeout: 10000
|
||||
}
|
||||
);
|
||||
|
||||
if (showResponse.data && showResponse.data.parameters) {
|
||||
const params = showResponse.data.parameters;
|
||||
// Extract context length from parameters (different models might use different parameter names)
|
||||
const contextWindow = params.context_length ||
|
||||
params.num_ctx ||
|
||||
params.context_window ||
|
||||
(LLM_CONSTANTS.OLLAMA_MODEL_CONTEXT_WINDOWS as Record<string, number>).default;
|
||||
|
||||
// Some models might provide embedding dimensions
|
||||
const embeddingDimension = params.embedding_length || params.dim || null;
|
||||
|
||||
log.info(`Fetched Ollama model info from API for ${modelName}: context window ${contextWindow}`);
|
||||
|
||||
return {
|
||||
dimension: embeddingDimension || 0, // We'll detect this separately if not provided
|
||||
contextWindow: contextWindow
|
||||
};
|
||||
}
|
||||
} catch (error: any) {
|
||||
log.info(`Could not fetch model info from Ollama show API: ${error.message}. Will try embedding test.`);
|
||||
// We'll fall back to embedding test if this fails
|
||||
}
|
||||
|
||||
return null;
|
||||
}
|
||||
|
||||
/**
|
||||
* Get model information by probing the API
|
||||
*/
|
||||
async getModelInfo(modelName: string): Promise<EmbeddingModelInfo> {
|
||||
// Check cache first
|
||||
if (this.modelInfoCache.has(modelName)) {
|
||||
return this.modelInfoCache.get(modelName)!;
|
||||
return this.modelInfoCache.get(modelName);
|
||||
}
|
||||
|
||||
// Default dimensions for common embedding models
|
||||
const defaultDimensions: Record<string, number> = {
|
||||
"nomic-embed-text": 768,
|
||||
"mxbai-embed-large": 1024,
|
||||
"llama3": 4096,
|
||||
"all-minilm": 384,
|
||||
"default": 4096
|
||||
};
|
||||
|
||||
// Default context windows
|
||||
const defaultContextWindows: Record<string, number> = {
|
||||
"nomic-embed-text": 8192,
|
||||
"mxbai-embed-large": 8192,
|
||||
"llama3": 8192,
|
||||
"all-minilm": 4096,
|
||||
"default": 4096
|
||||
};
|
||||
|
||||
// Try to fetch model capabilities from API
|
||||
const apiModelInfo = await this.fetchModelCapabilities(modelName);
|
||||
if (apiModelInfo) {
|
||||
// If we have context window but no embedding dimension, we need to detect the dimension
|
||||
if (apiModelInfo.contextWindow && !apiModelInfo.dimension) {
|
||||
try {
|
||||
// Try to detect if this is an embedding model
|
||||
// Detect dimension with a test embedding
|
||||
const dimension = await this.detectEmbeddingDimension(modelName);
|
||||
apiModelInfo.dimension = dimension;
|
||||
} catch (error) {
|
||||
// If dimension detection fails, fall back to defaults
|
||||
const baseModelName = modelName.split(':')[0];
|
||||
apiModelInfo.dimension = (LLM_CONSTANTS.OLLAMA_MODEL_DIMENSIONS as Record<string, number>)[baseModelName] ||
|
||||
(LLM_CONSTANTS.OLLAMA_MODEL_DIMENSIONS as Record<string, number>).default;
|
||||
}
|
||||
}
|
||||
|
||||
// Cache and return the API-provided info
|
||||
this.modelInfoCache.set(modelName, apiModelInfo);
|
||||
this.config.dimension = apiModelInfo.dimension;
|
||||
return apiModelInfo;
|
||||
}
|
||||
|
||||
// If API info fetch fails, fall back to test embedding
|
||||
try {
|
||||
const dimension = await this.detectEmbeddingDimension(modelName);
|
||||
const baseModelName = modelName.split(':')[0];
|
||||
const contextWindow = (LLM_CONSTANTS.OLLAMA_MODEL_CONTEXT_WINDOWS as Record<string, number>)[baseModelName] ||
|
||||
(LLM_CONSTANTS.OLLAMA_MODEL_CONTEXT_WINDOWS as Record<string, number>).default;
|
||||
|
||||
const modelInfo: EmbeddingModelInfo = { dimension, contextWindow };
|
||||
this.modelInfoCache.set(modelName, modelInfo);
|
||||
this.config.dimension = dimension;
|
||||
|
||||
log.info(`Detected Ollama model ${modelName} with dimension ${dimension} (context: ${contextWindow})`);
|
||||
return modelInfo;
|
||||
} catch (error: any) {
|
||||
log.error(`Error detecting Ollama model capabilities: ${error.message}`);
|
||||
|
||||
// If all detection fails, use defaults based on model name
|
||||
const baseModelName = modelName.split(':')[0];
|
||||
const dimension = (LLM_CONSTANTS.OLLAMA_MODEL_DIMENSIONS as Record<string, number>)[baseModelName] ||
|
||||
(LLM_CONSTANTS.OLLAMA_MODEL_DIMENSIONS as Record<string, number>).default;
|
||||
const contextWindow = (LLM_CONSTANTS.OLLAMA_MODEL_CONTEXT_WINDOWS as Record<string, number>)[baseModelName] ||
|
||||
(LLM_CONSTANTS.OLLAMA_MODEL_CONTEXT_WINDOWS as Record<string, number>).default;
|
||||
|
||||
log.info(`Using default parameters for model ${modelName}: dimension ${dimension}, context ${contextWindow}`);
|
||||
|
||||
const modelInfo: EmbeddingModelInfo = { dimension, contextWindow };
|
||||
this.modelInfoCache.set(modelName, modelInfo);
|
||||
this.config.dimension = dimension;
|
||||
|
||||
return modelInfo;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Detect embedding dimension by making a test API call
|
||||
*/
|
||||
private async detectEmbeddingDimension(modelName: string): Promise<number> {
|
||||
const testResponse = await axios.post(
|
||||
`${this.baseUrl}/api/embeddings`,
|
||||
{
|
||||
@ -82,46 +155,11 @@ export class OllamaEmbeddingProvider extends BaseEmbeddingProvider {
|
||||
}
|
||||
);
|
||||
|
||||
let dimension = 0;
|
||||
let contextWindow = 0;
|
||||
|
||||
if (testResponse.data && Array.isArray(testResponse.data.embedding)) {
|
||||
dimension = testResponse.data.embedding.length;
|
||||
|
||||
// Set context window based on model name if we have it
|
||||
const baseModelName = modelName.split(':')[0];
|
||||
contextWindow = defaultContextWindows[baseModelName] || defaultContextWindows.default;
|
||||
|
||||
log.info(`Detected Ollama model ${modelName} with dimension ${dimension}`);
|
||||
return testResponse.data.embedding.length;
|
||||
} else {
|
||||
throw new Error("Could not detect embedding dimensions");
|
||||
}
|
||||
|
||||
const modelInfo: EmbeddingModelInfo = { dimension, contextWindow };
|
||||
this.modelInfoCache.set(modelName, modelInfo);
|
||||
|
||||
// Update the provider config dimension
|
||||
this.config.dimension = dimension;
|
||||
|
||||
return modelInfo;
|
||||
} catch (error: any) {
|
||||
log.error(`Error detecting Ollama model capabilities: ${error.message}`);
|
||||
|
||||
// If detection fails, use defaults based on model name
|
||||
const baseModelName = modelName.split(':')[0];
|
||||
const dimension = defaultDimensions[baseModelName] || defaultDimensions.default;
|
||||
const contextWindow = defaultContextWindows[baseModelName] || defaultContextWindows.default;
|
||||
|
||||
log.info(`Using default dimension ${dimension} for model ${modelName}`);
|
||||
|
||||
const modelInfo: EmbeddingModelInfo = { dimension, contextWindow };
|
||||
this.modelInfoCache.set(modelName, modelInfo);
|
||||
|
||||
// Update the provider config dimension
|
||||
this.config.dimension = dimension;
|
||||
|
||||
return modelInfo;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
@ -136,6 +174,10 @@ export class OllamaEmbeddingProvider extends BaseEmbeddingProvider {
|
||||
*/
|
||||
async generateEmbeddings(text: string): Promise<Float32Array> {
|
||||
try {
|
||||
if (!text.trim()) {
|
||||
return new Float32Array(this.config.dimension);
|
||||
}
|
||||
|
||||
const modelName = this.config.model || "llama3";
|
||||
|
||||
// Ensure we have model info
|
||||
@ -173,29 +215,60 @@ export class OllamaEmbeddingProvider extends BaseEmbeddingProvider {
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* More specific implementation of batch size error detection for Ollama
|
||||
*/
|
||||
protected isBatchSizeError(error: any): boolean {
|
||||
const errorMessage = error?.message || '';
|
||||
const ollamaBatchSizeErrorPatterns = [
|
||||
'context length', 'token limit', 'out of memory',
|
||||
'too large', 'overloaded', 'prompt too long',
|
||||
'too many tokens', 'maximum size'
|
||||
];
|
||||
|
||||
return ollamaBatchSizeErrorPatterns.some(pattern =>
|
||||
errorMessage.toLowerCase().includes(pattern.toLowerCase())
|
||||
);
|
||||
}
|
||||
|
||||
/**
|
||||
* Generate embeddings for multiple texts
|
||||
*
|
||||
* Note: Ollama API doesn't support batch embedding, so we process them sequentially
|
||||
* but using the adaptive batch processor to handle rate limits and retries
|
||||
*/
|
||||
async generateBatchEmbeddings(texts: string[]): Promise<Float32Array[]> {
|
||||
if (texts.length === 0) {
|
||||
return [];
|
||||
}
|
||||
|
||||
try {
|
||||
return await this.processWithAdaptiveBatch(
|
||||
texts,
|
||||
async (batch) => {
|
||||
const results: Float32Array[] = [];
|
||||
|
||||
for (const text of texts) {
|
||||
try {
|
||||
// For Ollama, we have to process one at a time
|
||||
for (const text of batch) {
|
||||
// Skip empty texts
|
||||
if (!text.trim()) {
|
||||
results.push(new Float32Array(this.config.dimension));
|
||||
continue;
|
||||
}
|
||||
|
||||
const embedding = await this.generateEmbeddings(text);
|
||||
results.push(embedding);
|
||||
} catch (error: any) {
|
||||
const errorMessage = error.response?.data?.error?.message || error.message || "Unknown error";
|
||||
}
|
||||
|
||||
return results;
|
||||
},
|
||||
this.isBatchSizeError
|
||||
);
|
||||
}
|
||||
catch (error: any) {
|
||||
const errorMessage = error.message || "Unknown error";
|
||||
log.error(`Ollama batch embedding error: ${errorMessage}`);
|
||||
throw new Error(`Ollama batch embedding error: ${errorMessage}`);
|
||||
}
|
||||
}
|
||||
|
||||
return results;
|
||||
}
|
||||
}
|
||||
|
||||
@ -1,25 +1,165 @@
|
||||
import { BaseEmbeddingProvider } from "../base_embeddings.js";
|
||||
import type { EmbeddingConfig } from "../embeddings_interface.js";
|
||||
import axios from "axios";
|
||||
import log from "../../../log.js";
|
||||
|
||||
interface OpenAIEmbeddingConfig extends EmbeddingConfig {
|
||||
apiKey: string;
|
||||
baseUrl: string;
|
||||
}
|
||||
import { BaseEmbeddingProvider } from "../base_embeddings.js";
|
||||
import type { EmbeddingConfig, EmbeddingModelInfo } from "../embeddings_interface.js";
|
||||
import { LLM_CONSTANTS } from "../../../../routes/api/llm.js";
|
||||
|
||||
/**
|
||||
* OpenAI embedding provider implementation
|
||||
*/
|
||||
export class OpenAIEmbeddingProvider extends BaseEmbeddingProvider {
|
||||
name = "openai";
|
||||
private apiKey: string;
|
||||
private baseUrl: string;
|
||||
|
||||
constructor(config: OpenAIEmbeddingConfig) {
|
||||
constructor(config: EmbeddingConfig) {
|
||||
super(config);
|
||||
this.apiKey = config.apiKey;
|
||||
this.baseUrl = config.baseUrl;
|
||||
}
|
||||
|
||||
/**
|
||||
* Initialize the provider by detecting model capabilities
|
||||
*/
|
||||
async initialize(): Promise<void> {
|
||||
const modelName = this.config.model || "text-embedding-3-small";
|
||||
try {
|
||||
// Detect model capabilities
|
||||
const modelInfo = await this.getModelInfo(modelName);
|
||||
|
||||
// Update the config dimension
|
||||
this.config.dimension = modelInfo.dimension;
|
||||
|
||||
log.info(`OpenAI model ${modelName} initialized with dimension ${this.config.dimension} and context window ${modelInfo.contextWindow}`);
|
||||
} catch (error: any) {
|
||||
log.error(`Error initializing OpenAI provider: ${error.message}`);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Fetch model information from the OpenAI API
|
||||
*/
|
||||
private async fetchModelCapabilities(modelName: string): Promise<EmbeddingModelInfo | null> {
|
||||
if (!this.apiKey) {
|
||||
return null;
|
||||
}
|
||||
|
||||
try {
|
||||
// First try to get model details from the models API
|
||||
const response = await axios.get(
|
||||
`${this.baseUrl}/models/${modelName}`,
|
||||
{
|
||||
headers: {
|
||||
"Authorization": `Bearer ${this.apiKey}`,
|
||||
"Content-Type": "application/json"
|
||||
},
|
||||
timeout: 10000
|
||||
}
|
||||
);
|
||||
|
||||
if (response.data) {
|
||||
// Different model families may have different ways of exposing context window
|
||||
let contextWindow = 0;
|
||||
let dimension = 0;
|
||||
|
||||
// Extract context window if available
|
||||
if (response.data.context_window) {
|
||||
contextWindow = response.data.context_window;
|
||||
} else if (response.data.limits && response.data.limits.context_window) {
|
||||
contextWindow = response.data.limits.context_window;
|
||||
} else if (response.data.limits && response.data.limits.context_length) {
|
||||
contextWindow = response.data.limits.context_length;
|
||||
}
|
||||
|
||||
// Extract embedding dimensions if available
|
||||
if (response.data.dimensions) {
|
||||
dimension = response.data.dimensions;
|
||||
} else if (response.data.embedding_dimension) {
|
||||
dimension = response.data.embedding_dimension;
|
||||
}
|
||||
|
||||
// If we didn't get all the info, use defaults for missing values
|
||||
if (!contextWindow) {
|
||||
// Set default context window based on model name patterns
|
||||
if (modelName.includes('ada') || modelName.includes('embedding-ada')) {
|
||||
contextWindow = LLM_CONSTANTS.CONTEXT_WINDOW.OPENAI;
|
||||
} else if (modelName.includes('davinci')) {
|
||||
contextWindow = 8192;
|
||||
} else if (modelName.includes('embedding-3')) {
|
||||
contextWindow = 8191;
|
||||
} else {
|
||||
contextWindow = LLM_CONSTANTS.CONTEXT_WINDOW.OPENAI;
|
||||
}
|
||||
}
|
||||
|
||||
if (!dimension) {
|
||||
// Set default dimensions based on model name patterns
|
||||
if (modelName.includes('ada') || modelName.includes('embedding-ada')) {
|
||||
dimension = LLM_CONSTANTS.EMBEDDING_DIMENSIONS.OPENAI.ADA;
|
||||
} else if (modelName.includes('embedding-3-small')) {
|
||||
dimension = 1536;
|
||||
} else if (modelName.includes('embedding-3-large')) {
|
||||
dimension = 3072;
|
||||
} else {
|
||||
dimension = LLM_CONSTANTS.EMBEDDING_DIMENSIONS.OPENAI.DEFAULT;
|
||||
}
|
||||
}
|
||||
|
||||
log.info(`Fetched OpenAI model info for ${modelName}: context window ${contextWindow}, dimension ${dimension}`);
|
||||
|
||||
return {
|
||||
dimension,
|
||||
contextWindow
|
||||
};
|
||||
}
|
||||
} catch (error: any) {
|
||||
log.info(`Could not fetch model info from OpenAI API: ${error.message}. Will try embedding test.`);
|
||||
}
|
||||
|
||||
return null;
|
||||
}
|
||||
|
||||
/**
|
||||
* Get model information including embedding dimensions
|
||||
*/
|
||||
async getModelInfo(modelName: string): Promise<EmbeddingModelInfo> {
|
||||
// Check cache first
|
||||
if (this.modelInfoCache.has(modelName)) {
|
||||
return this.modelInfoCache.get(modelName);
|
||||
}
|
||||
|
||||
// Try to fetch model capabilities from API
|
||||
const apiModelInfo = await this.fetchModelCapabilities(modelName);
|
||||
if (apiModelInfo) {
|
||||
// Cache and return the API-provided info
|
||||
this.modelInfoCache.set(modelName, apiModelInfo);
|
||||
this.config.dimension = apiModelInfo.dimension;
|
||||
return apiModelInfo;
|
||||
}
|
||||
|
||||
// If API info fetch fails, try to detect embedding dimension with a test call
|
||||
try {
|
||||
const testEmbedding = await this.generateEmbeddings("Test");
|
||||
const dimension = testEmbedding.length;
|
||||
|
||||
// Use default context window
|
||||
let contextWindow = LLM_CONSTANTS.CONTEXT_WINDOW.OPENAI;
|
||||
|
||||
const modelInfo: EmbeddingModelInfo = { dimension, contextWindow };
|
||||
this.modelInfoCache.set(modelName, modelInfo);
|
||||
this.config.dimension = dimension;
|
||||
|
||||
log.info(`Detected OpenAI model ${modelName} with dimension ${dimension} (context: ${contextWindow})`);
|
||||
return modelInfo;
|
||||
} catch (error: any) {
|
||||
// If detection fails, use defaults
|
||||
const dimension = LLM_CONSTANTS.EMBEDDING_DIMENSIONS.OPENAI.DEFAULT;
|
||||
const contextWindow = LLM_CONSTANTS.CONTEXT_WINDOW.OPENAI;
|
||||
|
||||
log.info(`Using default parameters for OpenAI model ${modelName}: dimension ${dimension}, context ${contextWindow}`);
|
||||
|
||||
const modelInfo: EmbeddingModelInfo = { dimension, contextWindow };
|
||||
this.modelInfoCache.set(modelName, modelInfo);
|
||||
this.config.dimension = dimension;
|
||||
|
||||
return modelInfo;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
@ -27,6 +167,10 @@ export class OpenAIEmbeddingProvider extends BaseEmbeddingProvider {
|
||||
*/
|
||||
async generateEmbeddings(text: string): Promise<Float32Array> {
|
||||
try {
|
||||
if (!text.trim()) {
|
||||
return new Float32Array(this.config.dimension);
|
||||
}
|
||||
|
||||
const response = await axios.post(
|
||||
`${this.baseUrl}/embeddings`,
|
||||
{
|
||||
@ -43,8 +187,7 @@ export class OpenAIEmbeddingProvider extends BaseEmbeddingProvider {
|
||||
);
|
||||
|
||||
if (response.data && response.data.data && response.data.data[0] && response.data.data[0].embedding) {
|
||||
const embedding = response.data.data[0].embedding;
|
||||
return new Float32Array(embedding);
|
||||
return new Float32Array(response.data.data[0].embedding);
|
||||
} else {
|
||||
throw new Error("Unexpected response structure from OpenAI API");
|
||||
}
|
||||
@ -56,24 +199,33 @@ export class OpenAIEmbeddingProvider extends BaseEmbeddingProvider {
|
||||
}
|
||||
|
||||
/**
|
||||
* Generate embeddings for multiple texts in a single batch
|
||||
* More specific implementation of batch size error detection for OpenAI
|
||||
*/
|
||||
async generateBatchEmbeddings(texts: string[]): Promise<Float32Array[]> {
|
||||
protected isBatchSizeError(error: any): boolean {
|
||||
const errorMessage = error?.message || error?.response?.data?.error?.message || '';
|
||||
const openAIBatchSizeErrorPatterns = [
|
||||
'batch size', 'too many inputs', 'context length exceeded',
|
||||
'maximum context length', 'token limit', 'rate limit exceeded',
|
||||
'tokens in the messages', 'reduce the length', 'too long'
|
||||
];
|
||||
|
||||
return openAIBatchSizeErrorPatterns.some(pattern =>
|
||||
errorMessage.toLowerCase().includes(pattern.toLowerCase())
|
||||
);
|
||||
}
|
||||
|
||||
/**
|
||||
* Custom implementation for batched OpenAI embeddings
|
||||
*/
|
||||
async generateBatchEmbeddingsWithAPI(texts: string[]): Promise<Float32Array[]> {
|
||||
if (texts.length === 0) {
|
||||
return [];
|
||||
}
|
||||
|
||||
const batchSize = this.config.batchSize || 10;
|
||||
const results: Float32Array[] = [];
|
||||
|
||||
// Process in batches to avoid API limits
|
||||
for (let i = 0; i < texts.length; i += batchSize) {
|
||||
const batch = texts.slice(i, i + batchSize);
|
||||
try {
|
||||
const response = await axios.post(
|
||||
`${this.baseUrl}/embeddings`,
|
||||
{
|
||||
input: batch,
|
||||
input: texts,
|
||||
model: this.config.model || "text-embedding-3-small",
|
||||
encoding_format: "float"
|
||||
},
|
||||
@ -91,17 +243,49 @@ export class OpenAIEmbeddingProvider extends BaseEmbeddingProvider {
|
||||
.sort((a: any, b: any) => a.index - b.index)
|
||||
.map((item: any) => new Float32Array(item.embedding));
|
||||
|
||||
results.push(...sortedEmbeddings);
|
||||
return sortedEmbeddings;
|
||||
} else {
|
||||
throw new Error("Unexpected response structure from OpenAI API");
|
||||
}
|
||||
} catch (error: any) {
|
||||
const errorMessage = error.response?.data?.error?.message || error.message || "Unknown error";
|
||||
}
|
||||
|
||||
/**
|
||||
* Generate embeddings for multiple texts in a single batch
|
||||
* OpenAI API supports batch embedding, so we implement a custom version
|
||||
*/
|
||||
async generateBatchEmbeddings(texts: string[]): Promise<Float32Array[]> {
|
||||
if (texts.length === 0) {
|
||||
return [];
|
||||
}
|
||||
|
||||
try {
|
||||
return await this.processWithAdaptiveBatch(
|
||||
texts,
|
||||
async (batch) => {
|
||||
// Filter out empty texts and use the API batch functionality
|
||||
const filteredBatch = batch.filter(text => text.trim().length > 0);
|
||||
|
||||
if (filteredBatch.length === 0) {
|
||||
// If all texts are empty after filtering, return empty embeddings
|
||||
return batch.map(() => new Float32Array(this.config.dimension));
|
||||
}
|
||||
|
||||
if (filteredBatch.length === 1) {
|
||||
// If only one text, use the single embedding endpoint
|
||||
const embedding = await this.generateEmbeddings(filteredBatch[0]);
|
||||
return [embedding];
|
||||
}
|
||||
|
||||
// Use the batch API endpoint
|
||||
return this.generateBatchEmbeddingsWithAPI(filteredBatch);
|
||||
},
|
||||
this.isBatchSizeError
|
||||
);
|
||||
}
|
||||
catch (error: any) {
|
||||
const errorMessage = error.message || "Unknown error";
|
||||
log.error(`OpenAI batch embedding error: ${errorMessage}`);
|
||||
throw new Error(`OpenAI batch embedding error: ${errorMessage}`);
|
||||
}
|
||||
}
|
||||
|
||||
return results;
|
||||
}
|
||||
}
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user