HuggingFace

To use HuggingFace embeddings, you need to import HuggingFaceEmbedding from llamaindex.

import { HuggingFaceEmbedding, serviceContextFromDefaults } from "llamaindex";

const huggingFaceEmbeds = new HuggingFaceEmbedding();

const serviceContext = serviceContextFromDefaults({ embedModel: openaiEmbeds });

const document = new Document({ text: essay, id_: "essay" });

const index = await VectorStoreIndex.fromDocuments([document], {
  serviceContext,
});

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:

const embedModel = new HuggingFaceEmbedding({
  modelType: "BAAI/bge-small-en-v1.5",
  quantized: false,
});