{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "719619d3",
   "metadata": {},
   "source": [
    "# BGE Hugging Face Embeddings\n",
    "\n",
    "This notebook shows how to use BGE Embeddings through Hugging Face"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f7a54279",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# !pip install sentence_transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9e1d5b6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.embeddings import HuggingFaceBgeEmbeddings\n",
    "\n",
    "model_name = \"BAAI/bge-small-en\"\n",
    "model_kwargs = {'device': 'cpu'}\n",
    "encode_kwargs = {'normalize_embeddings': False}\n",
    "hf = HuggingFaceBgeEmbeddings(\n",
    "    model_name=model_name,\n",
    "    model_kwargs=model_kwargs,\n",
    "    encode_kwargs=encode_kwargs\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e59d1a89",
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding = hf.embed_query(\"hi this is harrison\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e596315f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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