langchain/docs/modules/models/text_embedding/examples/sentence_transformers.ipynb

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    "# Sentence Transformers Embeddings\n",
    "\n",
    "Let's generate embeddings using the [SentenceTransformers](https://www.sbert.net/) integration. SentenceTransformers is a python package that can generate text and image embeddings, originating from [Sentence-BERT](https://arxiv.org/abs/1908.10084)"
   ]
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   "execution_count": 7,
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     "text": [
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
     ]
    }
   ],
   "source": [
    "!pip install sentence_transformers > /dev/null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "861521a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.embeddings import SentenceTransformerEmbeddings "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ff9be586",
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings = SentenceTransformerEmbeddings(model=\"all-MiniLM-L6-v2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d0a98ae9",
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   "source": [
    "text = \"This is a test document.\""
   ]
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  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5d6c682b",
   "metadata": {},
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   "source": [
    "query_result = embeddings.embed_query(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bb5e74c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "doc_result = embeddings.embed_documents([text, \"This is not a test document.\"])"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "aaad49f8",
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