{
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  {
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   "cell_type": "markdown",
   "id": "ed47bb62",
   "metadata": {},
   "source": [
    "# Sentence Transformers Embeddings\n",
    "\n",
    "[SentenceTransformers](https://www.sbert.net/) embeddings are called using the `HuggingFaceEmbeddings` integration. We have also added an alias for `SentenceTransformerEmbeddings` for users who are more familiar with directly using that package.\n",
    "\n",
    "SentenceTransformers is a python package that can generate text and image embeddings, originating from [Sentence-BERT](https://arxiv.org/abs/1908.10084)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "06c9f47d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.1.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install sentence_transformers > /dev/null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "861521a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.embeddings import HuggingFaceEmbeddings, SentenceTransformerEmbeddings "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff9be586",
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings = HuggingFaceEmbeddings(model_name=\"all-MiniLM-L6-v2\")\n",
    "# Equivalent to SentenceTransformerEmbeddings(model_name=\"all-MiniLM-L6-v2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d0a98ae9",
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"This is a test document.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5d6c682b",
   "metadata": {},
   "outputs": [],
   "source": [
    "query_result = embeddings.embed_query(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bb5e74c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "doc_result = embeddings.embed_documents([text, \"This is not a test document.\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aaad49f8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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