Added Hugging face inference api (#10280)

Embed documents without locally downloading the HF model


---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
This commit is contained in:
JaéGeR
2023-09-07 03:25:48 +05:30
committed by GitHub
parent 6e6f15df24
commit b8669b249e
3 changed files with 169 additions and 14 deletions

View File

@@ -5,13 +5,23 @@
"id": "ed47bb62",
"metadata": {},
"source": [
"# Hugging Face Hub\n",
"# Hugging Face\n",
"Let's load the Hugging Face Embedding class."
]
},
{
"cell_type": "code",
"execution_count": 7,
"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",
"metadata": {},
"outputs": [],
@@ -21,7 +31,7 @@
},
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@@ -31,7 +41,7 @@
},
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"execution_count": 3,
"id": "d0a98ae9",
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@@ -41,7 +51,7 @@
},
{
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"execution_count": 5,
"id": "5d6c682b",
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@@ -51,7 +61,28 @@
},
{
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"execution_count": 6,
"id": "b57b8ce9-ef7d-4e63-979e-aa8763d1f9a8",
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{
"data": {
"text/plain": [
"[-0.04895168915390968, -0.03986193612217903, -0.021562768146395683]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query_result[:3]"
]
},
{
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"id": "bb5e74c0",
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@@ -60,19 +91,71 @@
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "aaad49f8",
"cell_type": "markdown",
"id": "92019ef1-5d30-4985-b4e6-c0d98bdfe265",
"metadata": {},
"outputs": [],
"source": []
"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."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "66f5c6ba-1446-43e1-b012-800d17cef300",
"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",
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"outputs": [
{
"data": {
"text/plain": [
"[-0.038338541984558105, 0.1234646737575531, -0.028642963618040085]"
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"execution_count": 4,
"metadata": {},
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}
],
"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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"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"display_name": "poetry-venv",
"language": "python",
"name": "python3"
"name": "poetry-venv"
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