langchain/docs/extras/integrations/text_embedding/instruct_embeddings.ipynb

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  {
   "cell_type": "markdown",
   "id": "59428e05",
   "metadata": {},
   "source": [
    "# InstructEmbeddings\n",
    "Let's load the HuggingFace instruct Embeddings class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "92c5b61e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.embeddings import HuggingFaceInstructEmbeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "062547b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load INSTRUCTOR_Transformer\n",
      "max_seq_length  512\n"
     ]
    }
   ],
   "source": [
    "embeddings = HuggingFaceInstructEmbeddings(\n",
    "    query_instruction=\"Represent the query for retrieval: \"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e1dcc4bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"This is a test document.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "90f0db94",
   "metadata": {},
   "outputs": [],
   "source": [
    "query_result = embeddings.embed_query(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aaad49f8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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