HuggingFace
To use HuggingFace embeddings, you need to import HuggingFaceEmbedding
from @llamaindex/huggingface
.
Installation
npm i llamaindex @llamaindex/huggingface
pnpm add llamaindex @llamaindex/huggingface
yarn add llamaindex @llamaindex/huggingface
bun add llamaindex @llamaindex/huggingface
import { 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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