From f2c66b0cb8056264b65881c195101bf647735035 Mon Sep 17 00:00:00 2001 From: ztimson Date: Wed, 11 Feb 2026 20:23:50 -0500 Subject: [PATCH] Updated default embedder --- package.json | 2 +- src/ai.ts | 2 ++ src/embedder.ts | 8 ++++---- src/llm.ts | 2 +- 4 files changed, 8 insertions(+), 6 deletions(-) diff --git a/package.json b/package.json index 984986e..04a2e2e 100644 --- a/package.json +++ b/package.json @@ -1,6 +1,6 @@ { "name": "@ztimson/ai-utils", - "version": "0.5.1", + "version": "0.5.2", "description": "AI Utility library", "author": "Zak Timson", "license": "MIT", diff --git a/src/ai.ts b/src/ai.ts index 8aeba6c..c681dad 100644 --- a/src/ai.ts +++ b/src/ai.ts @@ -10,6 +10,8 @@ export type AbortablePromise = Promise & { export type AiOptions = { /** Path to models */ path?: string; + /** Embedding model */ + embedder?: string; // all-MiniLM-L6-v2, bge-small-en-v1.5, bge-large-en-v1.5 /** Large language models, first is default */ llm?: Omit & { models: {[model: string]: AnthropicConfig | OllamaConfig | OpenAiConfig}; diff --git a/src/embedder.ts b/src/embedder.ts index 9b2c852..16dae3d 100644 --- a/src/embedder.ts +++ b/src/embedder.ts @@ -1,11 +1,11 @@ import { pipeline } from '@xenova/transformers'; import { parentPort } from 'worker_threads'; -let model: any; +let embedder: any; -parentPort?.on('message', async ({ id, text }) => { - if(!model) model = await pipeline('feature-extraction', 'Xenova/all-MiniLM-L6-v2'); - const output = await model(text, { pooling: 'mean', normalize: true }); +parentPort?.on('message', async ({ id, text, model }) => { + if(!embedder) embedder = await pipeline('feature-extraction', 'Xenova/' + model); + const output = await embedder(text, { pooling: 'mean', normalize: true }); const embedding = Array.from(output.data); parentPort?.postMessage({ id, embedding }); }); diff --git a/src/llm.ts b/src/llm.ts index 763d721..5db4d7e 100644 --- a/src/llm.ts +++ b/src/llm.ts @@ -271,7 +271,7 @@ class LLM { return new Promise((resolve, reject) => { const id = this.embedId++; this.embedQueue.set(id, { resolve, reject }); - this.embedWorker?.postMessage({ id, text }); + this.embedWorker?.postMessage({ id, text, model: this.ai.options?.embedder || 'bge-small-en-v1.5' }); }); }; const chunks = this.chunk(target, maxTokens, overlapTokens);