The library supports generating embeddings using the Hugging Face Inference API. By default it uses the sentence-transformers/distilbert-base-nli-mean-tokens model but you can pass it a model parameter to use any other model available on the Hugging Face model hub.

To use Hugging face embedding models via its Inference API, you need to get a free token. You can get this API Token by signing up for hugging face and visiting this page. Set it to the environment like so -

HUGGINGFACEHUB_API_KEY="<Your hf key>"

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Install Huggingface addon

npm install @llm-tools/embedjs-huggingface

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Usage

import { RAGApplicationBuilder } from '@llm-tools/embedjs';
import { HuggingFaceEmbeddings } from '@llm-tools/embedjs-huggingface';
import { HNSWDb } from '@llm-tools/embedjs-hnswlib';

const app = await new RAGApplicationBuilder()
.setEmbeddingModel(new HuggingFaceEmbeddings())

The library supports generating embeddings using the Hugging Face Inference API. By default it uses the sentence-transformers/distilbert-base-nli-mean-tokens model but you can pass it a model parameter to use any other model available on the Hugging Face model hub.

To use Hugging face embedding models via its Inference API, you need to get a free token. You can get this API Token by signing up for hugging face and visiting this page. Set it to the environment like so -

HUGGINGFACEHUB_API_KEY="<Your hf key>"

​
Install Huggingface addon

npm install @llm-tools/embedjs-huggingface

​
Usage

import { RAGApplicationBuilder } from '@llm-tools/embedjs';
import { HuggingFaceEmbeddings } from '@llm-tools/embedjs-huggingface';
import { HNSWDb } from '@llm-tools/embedjs-hnswlib';

const app = await new RAGApplicationBuilder()
.setEmbeddingModel(new HuggingFaceEmbeddings())