Demo1 - RAG with SageMaker and Kendra.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "c248797a-8e1e-4b59-af3f-689bf98445b7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "# Set the warning filter to ignore all warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8ccb2f5-6b69-494f-8888-8b0ffd33256f",
   "metadata": {},
   "source": [
    "# Part 1 - Data Preparation\n",
    "\n",
    "Let's import csv file, clean it and visualize data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "59734e8c-ffe0-4d72-9928-1339ead5f47c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import boto3, sagemaker, json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "85254bb2-1107-4523-b9fe-f7252b0d4eca",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "download: s3://reinvent-demo-vsemaan/united-states-inflation-rate-cpi.csv to ./united-states-inflation-rate-cpi.csv\n"
     ]
    }
   ],
   "source": [
    "s3_path = f\"s3://your-bucket/united-states-inflation-rate-cpi.csv\"\n",
    "!aws s3 cp $s3_path united-states-inflation-rate-cpi.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b8c475fd-4832-4ae1-a932-41d78dfa554e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# remove first 13 records from csv file\n",
    "!sed -i '1,14d' united-states-inflation-rate-cpi.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d7b9d278-e248-4b51-bffd-7456b7f4f9a9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th></th>\n",
       "      <th>.1</th>\n",
       "      <th>.2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>date</td>\n",
       "      <td>Inflation Rate (%)</td>\n",
       "      <td>Annual Change</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1960-12-31</td>\n",
       "      <td>1.458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1961-12-31</td>\n",
       "      <td>1.0707</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1962-12-31</td>\n",
       "      <td>1.1988</td>\n",
       "      <td>0.13</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1963-12-31</td>\n",
       "      <td>1.2397</td>\n",
       "      <td>0.04</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                                   .1   .2\n",
       "0        date   Inflation Rate (%)   Annual Change     \n",
       "1  1960-12-31                1.458             NaN  NaN\n",
       "2  1961-12-31               1.0707           -0.39  NaN\n",
       "3  1962-12-31               1.1988            0.13  NaN\n",
       "4  1963-12-31               1.2397            0.04  NaN"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the csv file into a dataframe\n",
    "df = pd.read_csv(\"united-states-inflation-rate-cpi.csv\")\n",
    "\n",
    "# show fitst 5 records\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e5265b1a-4036-4fcd-9075-534303ca58fe",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th></th>\n",
       "      <th>.1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>date</td>\n",
       "      <td>Inflation Rate (%)</td>\n",
       "      <td>Annual Change</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1960-12-31</td>\n",
       "      <td>1.458</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1961-12-31</td>\n",
       "      <td>1.0707</td>\n",
       "      <td>-0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1962-12-31</td>\n",
       "      <td>1.1988</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1963-12-31</td>\n",
       "      <td>1.2397</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                                   .1\n",
       "0        date   Inflation Rate (%)   Annual Change\n",
       "1  1960-12-31                1.458             NaN\n",
       "2  1961-12-31               1.0707           -0.39\n",
       "3  1962-12-31               1.1988            0.13\n",
       "4  1963-12-31               1.2397            0.04"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove the last column\n",
    "df = df.iloc[:, :-1]\n",
    "# show first 5 records\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "50b21ca7-f0a8-48bd-a695-453c1a022ba6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Unnamed: 0                                   .1\n",
      "0         date   Inflation Rate (%)   Annual Change\n",
      "1   1960-12-31                1.458               0\n",
      "2   1961-12-31               1.0707           -0.39\n",
      "3   1962-12-31               1.1988            0.13\n",
      "4   1963-12-31               1.2397            0.04\n",
      "..         ...                  ...             ...\n",
      "59  2018-12-31               2.4426            0.31\n",
      "60  2019-12-31               1.8122           -0.63\n",
      "61  2020-12-31               1.2336           -0.58\n",
      "62  2021-12-31               4.6979            3.46\n",
      "63  2022-12-31               8.0028             3.3\n",
      "\n",
      "[64 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "# replace NaN with 0\n",
    "df = df.fillna(0)\n",
    "print(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c86c24bd-d364-488e-ae08-ffbb2525288e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# use record 0 as column names\n",
    "df.columns = df.iloc[0]\n",
    "\n",
    "#remove fitst record\n",
    "df = df.iloc[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4ebe4760-1370-4f24-8154-e4f4704fd01d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0         date  Inflation Rate (%)  Annual Change\n",
      "1   1960-12-31               1.458              0\n",
      "2   1961-12-31              1.0707          -0.39\n",
      "3   1962-12-31              1.1988           0.13\n",
      "4   1963-12-31              1.2397           0.04\n",
      "5   1964-12-31              1.2789           0.04\n",
      "..         ...                 ...            ...\n",
      "59  2018-12-31              2.4426           0.31\n",
      "60  2019-12-31              1.8122          -0.63\n",
      "61  2020-12-31              1.2336          -0.58\n",
      "62  2021-12-31              4.6979           3.46\n",
      "63  2022-12-31              8.0028            3.3\n",
      "\n",
      "[63 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "79b360a0-5e62-4e98-b181-877308b768b8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3856/724973645.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df[' Inflation Rate (%)'] = pd.to_numeric(df[' Inflation Rate (%)'])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='date'>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#set second column as numeric\n",
    "df[' Inflation Rate (%)'] = pd.to_numeric(df[' Inflation Rate (%)'])\n",
    "# build chart that shows second column on y and date on x\n",
    "df.plot(x='date', y=' Inflation Rate (%)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "1a14829c-633f-413d-ade8-23db1ba01ab9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='date'>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#set last column as numeric\n",
    "df[' Annual Change'] = pd.to_numeric(df[' Annual Change'])\n",
    "\n",
    "# build chart that shows Inflation Rate and Annual Change over time\n",
    "df.plot(x='date', y=[' Annual Change', ' Inflation Rate (%)'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "41c3dbf2-5597-46dd-ac74-f5ab3a54c512",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# save the dataframe to a csv file\n",
    "df.to_csv(\"inflation_rate-updated.csv\", index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "9731c763-ea88-4680-ae2a-6cbc0b0f06cf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "upload: ./inflation_rate-updated.csv to s3://reinvent-demo-vsemaan/inflation_rate-updated.csv\n"
     ]
    }
   ],
   "source": [
    "# copy to s3 bucket\n",
    "s3_path = \"s3://your-bucket-name/inflation_rate-updated.csv\"\n",
    "!aws s3 cp inflation_rate-updated.csv $s3_path"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3fd23c3-0b55-4ade-b1a5-5310534e5c4f",
   "metadata": {},
   "source": [
    "# Part 2 - Deploy the model and invoke the endpoint\n",
    "\n",
    "Let's deploy LLAMA2 model using SageMaker JumpStart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8a18ca0-3085-4680-8cd7-23272dcb4ae1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# select a model \n",
    "model_id, model_version = \"meta-textgeneration-llama-2-7b-f\", \"*\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fb9519f-498c-4f9c-bab7-12b87c8cd86b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sagemaker.jumpstart.model import JumpStartModel\n",
    "\n",
    "model = JumpStartModel(model_id=model_id)\n",
    "predictor = model.deploy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "f545beed-bf83-4642-a722-40a3ed953d70",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#import dependencies\n",
    "import time\n",
    "import sagemaker, boto3, json\n",
    "from sagemaker.session import Session\n",
    "from sagemaker.model import Model\n",
    "from sagemaker import image_uris, model_uris, script_uris, hyperparameters\n",
    "from sagemaker.predictor import Predictor\n",
    "from sagemaker.utils import name_from_base\n",
    "from typing import Any, Dict, List, Optional\n",
    "from langchain.embeddings import SagemakerEndpointEmbeddings\n",
    "from langchain.llms.sagemaker_endpoint import ContentHandlerBase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "9c026c3f-1f51-4afe-b712-f9d2c557ea0f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sagemaker.jumpstart.model import JumpStartModel\n",
    "from langchain import SagemakerEndpoint\n",
    "\n",
    "llm_endpoint_name = \"your-endpoint\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "ade2ba55-ae91-4383-b1ad-f8d0b368bbb6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# define query format\n",
    "def query_endpoint(payload):\n",
    "    client = boto3.client(\"sagemaker-runtime\")\n",
    "    response = client.invoke_endpoint(\n",
    "        EndpointName=llm_endpoint_name,\n",
    "        ContentType=\"application/json\",\n",
    "        Body=json.dumps(payload),\n",
    "        CustomAttributes=\"accept_eula=true\",\n",
    "    )\n",
    "    response = response[\"Body\"].read().decode(\"utf8\")\n",
    "    response = json.loads(response)\n",
    "    return response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "feaa9880-1cd0-42fd-8173-08951f6cf5f0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# make response human readable\n",
    "def print_dialog(payload, response):\n",
    "    dialog = payload[\"inputs\"][0]\n",
    "    for msg in dialog:\n",
    "        print(f\"{msg['role'].capitalize()}: {msg['content']}\\n\")\n",
    "    print(f\"> {response[0]['generation']['role'].capitalize()}: {response[0]['generation']['content']}\")\n",
    "    print(\"\\n==================================\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "53c5d85d-9574-415f-a6ce-eedfd96d4e1b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "payload = {\n",
    "    \"inputs\": [[#import dependencies\n",
    "import time\n",
    "import sagemaker, boto3, json\n",
    "from sagemaker.session import Session\n",
    "from sagemaker.model import Model\n",
    "from sagemaker import image_uris, model_uris, script_uris, hyperparameters\n",
    "from sagemaker.predictor import Predictor\n",
    "from sagemaker.utils import name_from_base\n",
    "from typing import Any, Dict, List, Optional\n",
    "from langchain.embeddings import SagemakerEndpointEmbeddings\n",
    "from langchain.llms.sagemaker_endpoint import ContentHandlerBase\n",
    "        {\"role\": \"system\", \"content\": \"You are a helpful, respectful and honest assistant.\"},\n",
    "        {\"role\": \"user\", \"content\": \"\"},\n",
    "        \n",
    "    ]],\n",
    "    \"parameters\": {\"max_new_tokens\": 512, \"top_p\": 0.9, \"temperature\": 0.6}\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "8e442c4d-14f9-4cdb-893b-c98443e55045",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "System: You are a helpful, respectful and honest assistant.\n",
      "\n",
      "User: What was Inflation Rate in the US for year 2022?\n",
      "\n",
      "> Assistant:  Thank you for asking! According to the Bureau of Labor Statistics (BLS), the inflation rate in the United States for the year 2022 was 7.0% (as of December 2022). This means that the prices of goods and services in the US increased by 7.0% compared to the previous year. It's important to keep track of inflation rates to understand how it can affect your personal finances and investments. If you have any further questions, feel free to ask!\n",
      "\n",
      "==================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "response = query_endpoint(payload)\n",
    "print_dialog(payload,response)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c2a5e6d-b39f-41cc-9224-765bda11f818",
   "metadata": {},
   "source": [
    "# Part 3 - RAG workflow by combining the capabilities of Amazon Kendra with LLMs \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "f3a7f7c5-f4e2-45ef-b7a7-cd6b8f5f6b9c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# use Kendra Retriever to get top 3 pieces\n",
    "from langchain.retrievers import AmazonKendraRetriever\n",
    "retriever = AmazonKendraRetriever(index_id=\"YourKendraIndexID\",top_k=3)\n",
    "aws_region = \"your-aws-region\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "54829418-9f6f-484d-ac78-d519624fb3fa",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Defining prompt\n",
    "from langchain.prompts import PromptTemplate\n",
    "prompt_template = \"\"\"Use the following pieces of context to answer the question at the end. \n",
    "\n",
    "Context:  {context}\n",
    "\n",
    "Question: {question}\n",
    "Answer:\"\"\"\n",
    "\n",
    "PROMPT = PromptTemplate(template=prompt_template, input_variables=[\"context\", \"question\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "b6a8e72c-656d-4458-a08b-01c3bf010c8f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain import PromptTemplate, SagemakerEndpoint\n",
    "from langchain.llms.sagemaker_endpoint import LLMContentHandler\n",
    "import json\n",
    "\n",
    "class ContentHandler(LLMContentHandler):\n",
    "    content_type = \"application/json\"\n",
    "    accepts = \"application/json\"\n",
    "\n",
    "    def transform_input(self, prompt: str, model_kwargs: dict) -> bytes:\n",
    "        input_str = json.dumps({\"inputs\" : [[{\"role\" : \"system\",\n",
    "        \"content\" : \"You are an accurate information searching AI assitance.\"},\n",
    "        {\"role\" : \"user\", \"content\" : prompt}]],\n",
    "        \"parameters\" : {**model_kwargs}})\n",
    "        return input_str.encode('utf-8')\n",
    "    \n",
    "    def transform_output(self, output: bytes) -> str:\n",
    "        response_json = json.loads(output.read().decode(\"utf-8\"))\n",
    "        return response_json[0][\"generation\"][\"content\"]\n",
    "    \n",
    "\n",
    "parameters = {\n",
    "    \"max_new_tokens\": 500,\n",
    "    \"top_p\":0.5, \n",
    "    \"temperature\":0.01\n",
    "}\n",
    "\n",
    "content_handler = ContentHandler()\n",
    "\n",
    "sm_llm = SagemakerEndpoint(\n",
    "    endpoint_name = llm_endpoint_name, \n",
    "    region_name = aws_region, \n",
    "    model_kwargs = parameters,\n",
    "    endpoint_kwargs={\"CustomAttributes\": 'accept_eula=true'},\n",
    "    content_handler = content_handler,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "3bfebde9-4a59-4f1a-8e57-dcc77f4daeac",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.chains import RetrievalQA\n",
    "import langchain\n",
    "\n",
    "langchain.verbose = True\n",
    "chain_type_kwargs = {\"prompt\": PROMPT}\n",
    "qa = RetrievalQA.from_chain_type(llm=sm_llm, chain_type=\"stuff\", retriever=retriever, chain_type_kwargs=chain_type_kwargs, return_source_documents=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "68e0a4b0-8cfc-4282-ba14-95a0f85d08fc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new RetrievalQA chain...\u001b[0m\n",
      "\n",
      "\n",
      "\u001b[1m> Entering new StuffDocumentsChain chain...\u001b[0m\n",
      "\n",
      "\n",
      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
      "Prompt after formatting:\n",
      "\u001b[32;1m\u001b[1;3mUse the following pieces of context to answer the question at the end. \n",
      "\n",
      "Context:  Document Title: U.S. Inflation Rate 1960-2023 | MacroTrends\n",
      "Document Excerpt: \n",
      "Markets Market Indexes Precious Metals Energy Commodities Exchange Rates Interest Rates Futures Stock Research Top Stocks Stock Screener Top 100 Dividend Stocks Economy U.S. Economic Charts GDP by Country GDP Per Capita by Country Manufacturing by Country Debt to GDP Ratio by Country Global Metrics Topic Overview Largest Countries by Population U.S. States by Population U.S. and World Cities by Population U.S. Inflation Rate 1960-2023 Population Economy Trade Health Education Development Labor Force Environment Crime Immigration Other GDP GDP Growth Rate GDP Per Capita GNI GNI Per Capita Debt to GDP GNP Inflation Rate Economic Growth Manufacturing Inflation as measured by the consumer price index reflects the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services that may be fixed or changed at specified intervals, such as yearly. The Laspeyres formula is generally used. U.S. inflation rate for 2022 was 8.00%, a 3.3% increase from 2021. U.S. inflation rate for 2021 was 4.70%, a 3.46% increase from 2020. U.S. inflation rate for 2020 was 1.23%, a 0.58% decline from 2019. U.S. inflation rate for 2019 was 1.81%, a 0.63% decline from 2018. Download Historical Data Save as Image Data Source: World Bank MLA Citation: Similar Country Ranking Country Name Inflation Rate (%) Lithuania 19.71% Estonia 19.40% Latvia 17.31% Czech Republic 15.10% Hungary 14.61% Poland 14.43% Slovak Republic 12.77% Palau 12.35% Chile 11.64% Croatia 10.78% Netherlands 10.00% Greece 9.65% Belgium 9.60% Uruguay 9.10% Slovenia 8.83% Austria 8.55% Cyprus 8.40% Spain 8.39% Sweden 8.37% Iceland 8.31% Italy 8.20% United States 8.00% United Kingdom 7.92% Portugal 7.83% Ireland 7.81% Denmark 7.70\n",
      "\n",
      "\n",
      "Document Title: U.S. GNI 1962-2023 | MacroTrends\n",
      "Document Excerpt: \n",
      "value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. Data are in current U.S. dollars. GNI, calculated in national currency, is usually converted to U.S. dollars at official exchange rates for comparisons across economies, although an alternative rate is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate actually applied in international transactions. To smooth fluctuations in prices and exchange rates, a special Atlas method of conversion is used by the World Bank. This applies a conversion factor that averages the exchange rate for a given year and the two preceding years, adjusted for differences in rates of inflation between the country, and through 2000, the G-5 countries (France, Germany, Japan, the United Kingdom, and the United States). From 2001, these countries include the Euro area, Japan, the United Kingdom, and the United States. U.S. gni for 2022 was $25,454.40B, a 8.13% increase from 2021. U.S. gni for 2021 was $23,539.92B, a 9.83% increase from 2020. U.S. gni for 2020 was $21,432.32B, a 1.29% decline from 2019. U.S. gni for 2019 was $21,713.40B, a 4.69% increase from 2018. Download Historical Data Save as Image Data Source: World Bank MLA Citation: Similar Country Ranking Country Name Billions of US $ United States $25,454.40B Japan $5,310.03B Germany $4,488.99B United Kingdom $3,273.92B France $3,115.32B Italy $2,218.62B Canada $2,061.75B South Korea $1,858.05B Australia $1,569.90B Spain $1,508.54B Netherlands $1,016.73B Saudi Arabia $1,004.43B Switzerland $784.47B Poland $689.13B Sweden\n",
      "\n",
      "\n",
      "Document Title: U.S. GNP 1962-2023 | MacroTrends\n",
      "Document Excerpt: \n",
      "value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. Data are in current U.S. dollars. GNI, calculated in national currency, is usually converted to U.S. dollars at official exchange rates for comparisons across economies, although an alternative rate is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate actually applied in international transactions. To smooth fluctuations in prices and exchange rates, a special Atlas method of conversion is used by the World Bank. This applies a conversion factor that averages the exchange rate for a given year and the two preceding years, adjusted for differences in rates of inflation between the country, and through 2000, the G-5 countries (France, Germany, Japan, the United Kingdom, and the United States). From 2001, these countries include the Euro area, Japan, the United Kingdom, and the United States. U.S. gnp for 2022 was $25,454.40B, a 8.13% increase from 2021. U.S. gnp for 2021 was $23,539.92B, a 9.83% increase from 2020. U.S. gnp for 2020 was $21,432.32B, a 1.29% decline from 2019. U.S. gnp for 2019 was $21,713.40B, a 4.69% increase from 2018. Download Historical Data Save as Image Data Source: World Bank MLA Citation: Similar Country Ranking Country Name Billions of US $ United States $25,454.40B Japan $5,310.03B Germany $4,488.99B United Kingdom $3,273.92B France $3,115.32B Italy $2,218.62B Canada $2,061.75B South Korea $1,858.05B Australia $1,569.90B Spain $1,508.54B Netherlands $1,016.73B Saudi Arabia $1,004.43B Switzerland $784.47B Poland $689.13B Sweden\n",
      "\n",
      "\n",
      "Question: Inflation Rate in the US in 2022\n",
      "Answer:\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "query = \"Inflation Rate in the US in 2022\"\n",
    "result = qa(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "15b4da41-7aa2-400e-a931-c8449bd13f83",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " According to the document, the inflation rate in the US in 2022 was 8.00%, which is an increase of 3.3% from 2021.\n"
     ]
    }
   ],
   "source": [
    "print(result['result'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "006cf5f2-ec07-4225-9b7a-dc7c2a3f44b3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new RetrievalQA chain...\u001b[0m\n",
      "\n",
      "\n",
      "\u001b[1m> Entering new StuffDocumentsChain chain...\u001b[0m\n",
      "\n",
      "\n",
      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
      "Prompt after formatting:\n",
      "\u001b[32;1m\u001b[1;3mUse the following pieces of context to answer the question at the end. \n",
      "\n",
      "Context:  Document Title: Starbucks-FY22-Annual-Report.pdf\n",
      "Document Excerpt: \n",
      "Properties The material properties used by Starbucks in connection with its roasting, manufacturing, warehousing, distribution and corporate administrative operations, serving all segments, are as follows: Location Approximate Size in Square Feet Purpose York, PA 1,957,000 Roasting, warehousing and distribution Seattle, WA 1,145,000 Corporate administrative Minden, NV (Carson Valley) 1,080,000 Roasting, warehousing and distribution Lebanon, TN 680,000 Warehousing and distribution Kent, WA 510,000 Roasting and distribution Auburn, WA 491,000 Warehousing and distribution Shanghai, China 225,000 Corporate administrative We own most of our roasting facilities and lease the majority of our warehousing and distribution locations. As of October 2, 2022, Starbucks had 18,253 company-operated stores, almost all of which are leased. We also lease space in various locations worldwide for regional, district and other administrative offices, training facilities and storage. In addition to the locations listed above, we hold inventory at various locations managed by third-party warehouses. We believe our existing facilities, both owned and leased, are in good condition and suitable for the conduct of our business. Item 3. Legal Proceedings See Note 16, Commitments and Contingencies, to the consolidated financial statements included in Item 8 of Part II of this 10- K for information regarding certain legal proceedings in which we are involved. Item 4. Mine Safety Disclosures Not applicable. 22 Starbucks Corporation 2022 Form 10-K PART II Item 5.\n",
      "\n",
      "\n",
      "Document Title: Starbucks-FY22-Annual-Report.pdf\n",
      "Document Excerpt: \n",
      "The Starbucks Experience is built upon superior customer service, convenience and a seamless digital experience as well as safe, clean and well-maintained stores that reflect the personalities of the communities in which they operate, thereby building a high degree of customer loyalty. Our strategy for expanding our global retail business is to increase our category share in a disciplined manner, by selectively opening additional stores in new and existing markets, as well as increasing sales in existing stores, to support our long-term strategic objective to maintain Starbucks standing as one of the most recognized and respected brands in the world. Store growth in specific existing markets will vary due to many factors, including expected financial returns, the maturity of the market, economic conditions, consumer behavior and local business environment. in existing stores, to support our long-term strategic objective to maintain Starbucks standing as one of the most recognized and respected brands in the world. Store growth in specific existing markets will vary due to many factors, including expected financial returns, the maturity of the market, economic conditions, consumer behavior and local business environment. Company-operated store data for the fiscal year-ended October 2, 2022: Stores Open as of Stores Open as of Oct 3, 2021 Opened Closed Transfers Net Oct 2, 2022 North America: U.S. 8,947 437 (116) (3) 318 9,265 Canada 908 44 (6) — 38 946 Siren Retail 6 — (1) — (1) 5 Total North America Company-operated store data for the fiscal year-ended October 2, 2022: Stores Open as of Stores Open as of Oct 3, 2021 Opened Closed Transfers Net Oct 2, 2022 North America: U.S. 8,947 437 (116) (3) 318 9,265 Canada 908 44 (6) — 38 946 Siren Retail 6 — (1) — (1) 5 Total North America 9,861 481 (123) (3) 355 10,216 International: China 5,358 724 (63) — 661 6,019 Japan 1,546 102 (18) — 84 1,630 U.K. 298 29 (6) (3) 20 318 All Other 65 2 (2) — — 65 Siren Retail 5 — — — — 5 Total International 7,272 857 (89) (3) 765\n",
      "\n",
      "\n",
      "Document Title: Starbucks-FY22-Annual-Report.pdf\n",
      "Document Excerpt: \n",
      "As part of these arrangements, we sell coffee, tea, food and related products to licensees for resale to customers and receive royalties and license fees from the licensees. We also sell certain equipment, such as coffee brewers and espresso machines, to our licensees for use in their operations. Licensee employees working in licensed retail locations are required to follow our detailed store operating procedures and attend training classes similar to those given to employees in company-operated stores. In a limited number of international markets, we also use traditional franchising and include these stores in the results of operations from our other licensed stores. Starbucks Corporation 2022 Form 10-K 7 Licensed store data for the fiscal year-ended October 2, 2022: Stores Open as of Stores Open as of Oct 3, 2021 Opened Closed Transfers Net Oct Starbucks Corporation 2022 Form 10-K 7 Licensed store data for the fiscal year-ended October 2, 2022: Stores Open as of Stores Open as of Oct 3, 2021 Opened Closed Transfers Net Oct 2, 2022 North America: U.S. 6,497 217 (109) 3 111 6,608 Canada 468 27 (24) — 3 471 Total North America 6,965 244 (133) 3 114 7,079 International: Korea 1,611 168 (29) — 139 1,750 Latin America 1,437 115 (3) — 112 1,549 U.K. 791 74 (30) 3 47 838 Turkey 559 50 (5\n",
      "\n",
      "\n",
      "Question: How many Starbucks stores opened in 2022\n",
      "Answer:\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      " According to the information provided in the document, Starbucks opened 437 new stores in North America in 2022, and 724 new stores in international markets, for a total of 1,151 new stores opened in 2022.\n"
     ]
    }
   ],
   "source": [
    "query = \"\"\n",
    "result = qa(query)\n",
    "print(result['result'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe21a6d0-0a31-4670-82e6-5dd21d88e5db",
   "metadata": {},
   "outputs": [],
   "source": [
    "# use codewhisperer promptwill be hereandhher "
   ]
  }
 ],
 "metadata": {
  "availableInstances": [
   {
    "_defaultOrder": 0,
    "_isFastLaunch": true,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 4,
    "name": "ml.t3.medium",
    "vcpuNum": 2
   },
   {
    "_defaultOrder": 1,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 8,
    "name": "ml.t3.large",
    "vcpuNum": 2
   },
   {
    "_defaultOrder": 2,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 16,
    "name": "ml.t3.xlarge",
    "vcpuNum": 4
   },
   {
    "_defaultOrder": 3,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 32,
    "name": "ml.t3.2xlarge",
    "vcpuNum": 8
   },
   {
    "_defaultOrder": 4,
    "_isFastLaunch": true,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 8,
    "name": "ml.m5.large",
    "vcpuNum": 2
   },
   {
    "_defaultOrder": 5,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 16,
    "name": "ml.m5.xlarge",
    "vcpuNum": 4
   },
   {
    "_defaultOrder": 6,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 32,
    "name": "ml.m5.2xlarge",
    "vcpuNum": 8
   },
   {
    "_defaultOrder": 7,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 64,
    "name": "ml.m5.4xlarge",
    "vcpuNum": 16
   },
   {
    "_defaultOrder": 8,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 128,
    "name": "ml.m5.8xlarge",
    "vcpuNum": 32
   },
   {
    "_defaultOrder": 9,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 192,
    "name": "ml.m5.12xlarge",
    "vcpuNum": 48
   },
   {
    "_defaultOrder": 10,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 256,
    "name": "ml.m5.16xlarge",
    "vcpuNum": 64
   },
   {
    "_defaultOrder": 11,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 384,
    "name": "ml.m5.24xlarge",
    "vcpuNum": 96
   },
   {
    "_defaultOrder": 12,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 8,
    "name": "ml.m5d.large",
    "vcpuNum": 2
   },
   {
    "_defaultOrder": 13,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 16,
    "name": "ml.m5d.xlarge",
    "vcpuNum": 4
   },
   {
    "_defaultOrder": 14,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 32,
    "name": "ml.m5d.2xlarge",
    "vcpuNum": 8
   },
   {
    "_defaultOrder": 15,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 64,
    "name": "ml.m5d.4xlarge",
    "vcpuNum": 16
   },
   {
    "_defaultOrder": 16,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 128,
    "name": "ml.m5d.8xlarge",
    "vcpuNum": 32
   },
   {
    "_defaultOrder": 17,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 192,
    "name": "ml.m5d.12xlarge",
    "vcpuNum": 48
   },
   {
    "_defaultOrder": 18,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 256,
    "name": "ml.m5d.16xlarge",
    "vcpuNum": 64
   },
   {
    "_defaultOrder": 19,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 384,
    "name": "ml.m5d.24xlarge",
    "vcpuNum": 96
   },
   {
    "_defaultOrder": 20,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": true,
    "memoryGiB": 0,
    "name": "ml.geospatial.interactive",
    "supportedImageNames": [
     "sagemaker-geospatial-v1-0"
    ],
    "vcpuNum": 0
   },
   {
    "_defaultOrder": 21,
    "_isFastLaunch": true,
    "category": "Compute optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 4,
    "name": "ml.c5.large",
    "vcpuNum": 2
   },
   {
    "_defaultOrder": 22,
    "_isFastLaunch": false,
    "category": "Compute optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 8,
    "name": "ml.c5.xlarge",
    "vcpuNum": 4
   },
   {
    "_defaultOrder": 23,
    "_isFastLaunch": false,
    "category": "Compute optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 16,
    "name": "ml.c5.2xlarge",
    "vcpuNum": 8
   },
   {
    "_defaultOrder": 24,
    "_isFastLaunch": false,
    "category": "Compute optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 32,
    "name": "ml.c5.4xlarge",
    "vcpuNum": 16
   },
   {
    "_defaultOrder": 25,
    "_isFastLaunch": false,
    "category": "Compute optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 72,
    "name": "ml.c5.9xlarge",
    "vcpuNum": 36
   },
   {
    "_defaultOrder": 26,
    "_isFastLaunch": false,
    "category": "Compute optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 96,
    "name": "ml.c5.12xlarge",
    "vcpuNum": 48
   },
   {
    "_defaultOrder": 27,
    "_isFastLaunch": false,
    "category": "Compute optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 144,
    "name": "ml.c5.18xlarge",
    "vcpuNum": 72
   },
   {
    "_defaultOrder": 28,
    "_isFastLaunch": false,
    "category": "Compute optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 192,
    "name": "ml.c5.24xlarge",
    "vcpuNum": 96
   },
   {
    "_defaultOrder": 29,
    "_isFastLaunch": true,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 16,
    "name": "ml.g4dn.xlarge",
    "vcpuNum": 4
   },
   {
    "_defaultOrder": 30,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 32,
    "name": "ml.g4dn.2xlarge",
    "vcpuNum": 8
   },
   {
    "_defaultOrder": 31,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 64,
    "name": "ml.g4dn.4xlarge",
    "vcpuNum": 16
   },
   {
    "_defaultOrder": 32,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 128,
    "name": "ml.g4dn.8xlarge",
    "vcpuNum": 32
   },
   {
    "_defaultOrder": 33,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 4,
    "hideHardwareSpecs": false,
    "memoryGiB": 192,
    "name": "ml.g4dn.12xlarge",
    "vcpuNum": 48
   },
   {
    "_defaultOrder": 34,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 256,
    "name": "ml.g4dn.16xlarge",
    "vcpuNum": 64
   },
   {
    "_defaultOrder": 35,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 61,
    "name": "ml.p3.2xlarge",
    "vcpuNum": 8
   },
   {
    "_defaultOrder": 36,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 4,
    "hideHardwareSpecs": false,
    "memoryGiB": 244,
    "name": "ml.p3.8xlarge",
    "vcpuNum": 32
   },
   {
    "_defaultOrder": 37,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 8,
    "hideHardwareSpecs": false,
    "memoryGiB": 488,
    "name": "ml.p3.16xlarge",
    "vcpuNum": 64
   },
   {
    "_defaultOrder": 38,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 8,
    "hideHardwareSpecs": false,
    "memoryGiB": 768,
    "name": "ml.p3dn.24xlarge",
    "vcpuNum": 96
   },
   {
    "_defaultOrder": 39,
    "_isFastLaunch": false,
    "category": "Memory Optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 16,
    "name": "ml.r5.large",
    "vcpuNum": 2
   },
   {
    "_defaultOrder": 40,
    "_isFastLaunch": false,
    "category": "Memory Optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 32,
    "name": "ml.r5.xlarge",
    "vcpuNum": 4
   },
   {
    "_defaultOrder": 41,
    "_isFastLaunch": false,
    "category": "Memory Optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 64,
    "name": "ml.r5.2xlarge",
    "vcpuNum": 8
   },
   {
    "_defaultOrder": 42,
    "_isFastLaunch": false,
    "category": "Memory Optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 128,
    "name": "ml.r5.4xlarge",
    "vcpuNum": 16
   },
   {
    "_defaultOrder": 43,
    "_isFastLaunch": false,
    "category": "Memory Optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 256,
    "name": "ml.r5.8xlarge",
    "vcpuNum": 32
   },
   {
    "_defaultOrder": 44,
    "_isFastLaunch": false,
    "category": "Memory Optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 384,
    "name": "ml.r5.12xlarge",
    "vcpuNum": 48
   },
   {
    "_defaultOrder": 45,
    "_isFastLaunch": false,
    "category": "Memory Optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 512,
    "name": "ml.r5.16xlarge",
    "vcpuNum": 64
   },
   {
    "_defaultOrder": 46,
    "_isFastLaunch": false,
    "category": "Memory Optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 768,
    "name": "ml.r5.24xlarge",
    "vcpuNum": 96
   },
   {
    "_defaultOrder": 47,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 16,
    "name": "ml.g5.xlarge",
    "vcpuNum": 4
   },
   {
    "_defaultOrder": 48,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 32,
    "name": "ml.g5.2xlarge",
    "vcpuNum": 8
   },
   {
    "_defaultOrder": 49,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 64,
    "name": "ml.g5.4xlarge",
    "vcpuNum": 16
   },
   {
    "_defaultOrder": 50,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 128,
    "name": "ml.g5.8xlarge",
    "vcpuNum": 32
   },
   {
    "_defaultOrder": 51,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 256,
    "name": "ml.g5.16xlarge",
    "vcpuNum": 64
   },
   {
    "_defaultOrder": 52,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 4,
    "hideHardwareSpecs": false,
    "memoryGiB": 192,
    "name": "ml.g5.12xlarge",
    "vcpuNum": 48
   },
   {
    "_defaultOrder": 53,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 4,
    "hideHardwareSpecs": false,
    "memoryGiB": 384,
    "name": "ml.g5.24xlarge",
    "vcpuNum": 96
   },
   {
    "_defaultOrder": 54,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 8,
    "hideHardwareSpecs": false,
    "memoryGiB": 768,
    "name": "ml.g5.48xlarge",
    "vcpuNum": 192
   },
   {
    "_defaultOrder": 55,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 8,
    "hideHardwareSpecs": false,
    "memoryGiB": 1152,
    "name": "ml.p4d.24xlarge",
    "vcpuNum": 96
   },
   {
    "_defaultOrder": 56,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 8,
    "hideHardwareSpecs": false,
    "memoryGiB": 1152,
    "name": "ml.p4de.24xlarge",
    "vcpuNum": 96
   },
   {
    "_defaultOrder": 57,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 32,
    "name": "ml.trn1.2xlarge",
    "vcpuNum": 8
   },
   {
    "_defaultOrder": 58,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 512,
    "name": "ml.trn1.32xlarge",
    "vcpuNum": 128
   },
   {
    "_defaultOrder": 59,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 512,
    "name": "ml.trn1n.32xlarge",
    "vcpuNum": 128
   }
  ],
  "instance_type": "ml.t3.medium",
  "kernelspec": {
   "display_name": "Python 3 (Data Science 3.0)",
   "language": "python",
   "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-west-2:236514542706:image/sagemaker-data-science-310-v1"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}