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{
"cells": [
{
"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": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
},
"vscode": {
"interpreter": {
"hash": "7377c2ccc78bc62c2683122d48c8cd1fb85a53850a1b1fc29736ed39852c9885"
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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