{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ed47bb62",
   "metadata": {},
   "source": [
    "# Hugging Face Hub\n",
    "Let's load the Hugging Face Embedding class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "861521a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.embeddings import HuggingFaceEmbeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ff9be586",
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings = HuggingFaceEmbeddings()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d0a98ae9",
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"This is a test document.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5d6c682b",
   "metadata": {},
   "outputs": [],
   "source": [
    "query_result = embeddings.embed_query(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bb5e74c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "doc_result = embeddings.embed_documents([text])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aaad49f8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  },
  "vscode": {
   "interpreter": {
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 "nbformat": 4,
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}