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{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"<a href=\"https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/embeddings/sagemaker_embedding_endpoint.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Interacting with Embeddings deployed in Amazon SageMaker Endpoint with LlamaIndex\n",
"\n",
"An Amazon SageMaker endpoint is a fully managed resource that enables the deployment of machine learning models, for making predictions on new data.\n",
"\n",
"This notebook demonstrates how to interact with Embedding endpoints using `SageMakerEmbedding`, unlocking additional llamaIndex features.\n",
"So, It is assumed that an Embedding is deployed on a SageMaker endpoint."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setting Up\n",
"If you’re opening this Notebook on colab, you will probably need to install LlamaIndex 🦙. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%pip install llama-index-embeddings-sagemaker-endpoint"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"! pip install llama-index"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You have to specify the endpoint name to interact with."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ENDPOINT_NAME = \"<-YOUR-ENDPOINT-NAME->\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Credentials should be provided to connect to the endpoint. You can either:\n",
"- use an AWS profile by specifying the `profile_name` parameter, if not specified, the default credential profile will be used. \n",
"- Pass credentials as parameters (`aws_access_key_id`, `aws_secret_access_key`, `aws_session_token`, `region_name`). \n",
"\n",
"for more details check [this link](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**AWS profile name**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from llama_index.embeddings.sagemaker_endpoint import SageMakerEmbedding\n",
"\n",
"AWS_ACCESS_KEY_ID = \"<-YOUR-AWS-ACCESS-KEY-ID->\"\n",
"AWS_SECRET_ACCESS_KEY = \"<-YOUR-AWS-SECRET-ACCESS-KEY->\"\n",
"AWS_SESSION_TOKEN = \"<-YOUR-AWS-SESSION-TOKEN->\"\n",
"REGION_NAME = \"<-YOUR-ENDPOINT-REGION-NAME->\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"embed_model = SageMakerEmbedding(\n",
" endpoint_name=ENDPOINT_NAME,\n",
" aws_access_key_id=AWS_ACCESS_KEY_ID,\n",
" aws_secret_access_key=AWS_SECRET_ACCESS_KEY,\n",
" aws_session_token=AWS_SESSION_TOKEN,\n",
" region_name=REGION_NAME,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**With credentials**:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from llama_index.embeddings.sagemaker_endpoint import SageMakerEmbedding\n",
"\n",
"ENDPOINT_NAME = \"<-YOUR-ENDPOINT-NAME->\"\n",
"PROFILE_NAME = \"<-YOUR-PROFILE-NAME->\"\n",
"embed_model = SageMakerEmbedding(\n",
" endpoint_name=ENDPOINT_NAME, profile_name=PROFILE_NAME\n",
") # Omit the profile name to use the default profile"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Basic Usage"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Call `get_text_embedding` "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"embeddings = embed_model.get_text_embedding(\n",
" \"An Amazon SageMaker endpoint is a fully managed resource that enables the deployment of machine learning models, specifically LLM (Large Language Models), for making predictions on new data.\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
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"source": [
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{
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"metadata": {},
"source": [
"### Call `get_text_embedding_batch` "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"embeddings = embed_model.get_text_embedding_batch(\n",
" [\n",
" \"An Amazon SageMaker endpoint is a fully managed resource that enables the deployment of machine learning models\",\n",
" \"Sagemaker is integrated with llamaIndex\",\n",
" ]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(embeddings)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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"
},
"vscode": {
"interpreter": {
"hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e"
}
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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