HuggingFace
To use HuggingFace embeddings, you need to import HuggingFaceEmbedding from @llamaindex/huggingface.
Installation
npm i llamaindex @llamaindex/huggingfacepnpm add llamaindex @llamaindex/huggingfaceyarn add llamaindex @llamaindex/huggingfacebun add llamaindex @llamaindex/huggingfaceimport { Document, Settings, VectorStoreIndex } from "llamaindex"; import { HuggingFaceEmbedding } from "@llamaindex/huggingface"; // Update Embed Model Settings.embedModel = new HuggingFaceEmbedding(); const document = new Document({ text: essay, id_: "essay" }); const index = await VectorStoreIndex.fromDocuments([document]); const queryEngine = index.asQueryEngine(); const query = "What is the meaning of life?"; const results = await queryEngine.query({ query, });Per default, HuggingFaceEmbedding is using the Xenova/all-MiniLM-L6-v2 model. You can change the model by passing the modelType parameter to the constructor. If you're not using a quantized model, set the quantized parameter to false.
For example, to use the not quantized BAAI/bge-small-en-v1.5 model, you can use the following code:
import { HuggingFaceEmbedding } from "@llamaindex/huggingface"; Settings.embedModel = new HuggingFaceEmbedding({ modelType: "BAAI/bge-small-en-v1.5", quantized: false, });API Reference
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