{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "59428e05",
   "metadata": {},
   "source": [
    "# Instruct Embeddings on Hugging Face\n",
    "\n",
    ">[Hugging Face sentence-transformers](https://huggingface.co/sentence-transformers) is a Python framework for state-of-the-art sentence, text and image embeddings.\n",
    ">One of the instruct embedding models is used in the `HuggingFaceInstructEmbeddings` class.\n"
   ]
  },
  {
   "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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