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99 lines
1.9 KiB
Plaintext
99 lines
1.9 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "59428e05",
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"metadata": {},
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"source": [
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"# HuggingFace Instruct\n",
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"Let's load the `HuggingFace instruct Embeddings` class."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "92c5b61e",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.embeddings import HuggingFaceInstructEmbeddings"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "062547b9",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"load INSTRUCTOR_Transformer\n",
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"max_seq_length 512\n"
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]
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}
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],
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"source": [
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"embeddings = HuggingFaceInstructEmbeddings(\n",
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" query_instruction=\"Represent the query for retrieval: \"\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "e1dcc4bd",
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"metadata": {},
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"outputs": [],
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"source": [
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"text = \"This is a test document.\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "90f0db94",
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"metadata": {},
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"outputs": [],
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"source": [
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"query_result = embeddings.embed_query(text)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "aaad49f8",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.6"
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},
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"vscode": {
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"interpreter": {
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"hash": "7377c2ccc78bc62c2683122d48c8cd1fb85a53850a1b1fc29736ed39852c9885"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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