mirror of
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129 lines
3.1 KiB
Plaintext
129 lines
3.1 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "1f83f273",
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"metadata": {},
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"source": [
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"# SageMaker Endpoint Embeddings\n",
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"\n",
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"Let's load the SageMaker Endpoints Embeddings class. The class can be used if you host, e.g. your own Hugging Face model on SageMaker.\n",
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"\n",
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"For instrucstions on how to do this, please see [here](https://www.philschmid.de/custom-inference-huggingface-sagemaker)"
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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": "88d366bd",
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip3 install langchain boto3"
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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": 3,
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"id": "1e9b926a",
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"metadata": {},
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"outputs": [],
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"source": [
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"from typing import Dict\n",
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"from langchain.embeddings import SagemakerEndpointEmbeddings\n",
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"from langchain.llms.sagemaker_endpoint import ContentHandlerBase\n",
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"import json\n",
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"\n",
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"\n",
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"class ContentHandler(ContentHandlerBase):\n",
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" content_type = \"application/json\"\n",
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" accepts = \"application/json\"\n",
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"\n",
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" def transform_input(self, prompt: str, model_kwargs: Dict) -> bytes:\n",
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" input_str = json.dumps({\"inputs\": prompt, **model_kwargs})\n",
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" return input_str.encode(\"utf-8\")\n",
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"\n",
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" def transform_output(self, output: bytes) -> str:\n",
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" response_json = json.loads(output.read().decode(\"utf-8\"))\n",
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" return response_json[\"embeddings\"]\n",
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"\n",
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"\n",
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"content_handler = ContentHandler()\n",
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"\n",
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"\n",
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"embeddings = SagemakerEndpointEmbeddings(\n",
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" # endpoint_name=\"endpoint-name\",\n",
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" # credentials_profile_name=\"credentials-profile-name\",\n",
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" endpoint_name=\"huggingface-pytorch-inference-2023-03-21-16-14-03-834\",\n",
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" region_name=\"us-east-1\",\n",
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" content_handler=content_handler,\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": null,
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"id": "fe9797b8",
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"metadata": {},
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"outputs": [],
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"source": [
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"query_result = embeddings.embed_query(\"foo\")"
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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": 6,
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"id": "76f1b752",
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"metadata": {},
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"outputs": [],
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"source": [
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"doc_results = embeddings.embed_documents([\"foo\"])"
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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": "fff99b21",
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"metadata": {},
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"outputs": [],
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"source": [
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"doc_results"
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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": "3.9.1"
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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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