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

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{
"cells": [
{
"cell_type": "markdown",
"id": "ed47bb62",
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"metadata": {},
"source": [
"# Hugging Face\n",
"Let's load the Hugging Face Embedding class."
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]
},
{
"cell_type": "code",
"execution_count": null,
"id": "16b20335-da1d-46ba-aa23-fbf3e2c6fe60",
"metadata": {},
"outputs": [],
"source": [
"!pip install langchain sentence_transformers"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "861521a9",
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"metadata": {},
"outputs": [],
"source": [
"from langchain.embeddings import HuggingFaceEmbeddings"
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]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "ff9be586",
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"metadata": {},
"outputs": [],
"source": [
"embeddings = HuggingFaceEmbeddings()"
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]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "d0a98ae9",
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"metadata": {},
"outputs": [],
"source": [
"text = \"This is a test document.\""
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]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "5d6c682b",
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"metadata": {},
"outputs": [],
"source": [
"query_result = embeddings.embed_query(text)"
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]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "b57b8ce9-ef7d-4e63-979e-aa8763d1f9a8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[-0.04895168915390968, -0.03986193612217903, -0.021562768146395683]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query_result[:3]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "bb5e74c0",
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"metadata": {},
"outputs": [],
"source": [
"doc_result = embeddings.embed_documents([text])"
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]
},
{
"cell_type": "markdown",
"id": "92019ef1-5d30-4985-b4e6-c0d98bdfe265",
"metadata": {},
"source": [
"## Hugging Face Inference API\n",
"We can also access embedding models via the Hugging Face Inference API, which does not require us to install ``sentence_transformers`` and download models locally."
]
},
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{
"cell_type": "code",
"execution_count": 1,
"id": "66f5c6ba-1446-43e1-b012-800d17cef300",
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"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"Enter your HF Inference API Key:\n",
"\n",
" ········\n"
]
}
],
"source": [
"import getpass\n",
"\n",
"inference_api_key = getpass.getpass(\"Enter your HF Inference API Key:\\n\\n\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d0623c1f-cd82-4862-9bce-3655cb9b66ac",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[-0.038338541984558105, 0.1234646737575531, -0.028642963618040085]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain.embeddings import HuggingFaceInferenceAPIEmbeddings\n",
"\n",
"embeddings = HuggingFaceInferenceAPIEmbeddings(\n",
" api_key=inference_api_key,\n",
" model_name=\"sentence-transformers/all-MiniLM-l6-v2\"\n",
")\n",
"\n",
"query_result = embeddings.embed_query(text)\n",
"query_result[:3]"
]
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}
],
"metadata": {
"kernelspec": {
"display_name": "poetry-venv",
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"language": "python",
"name": "poetry-venv"
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},
"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"
}
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}
},
"nbformat": 4,
"nbformat_minor": 5
}