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Merge #20 Incorporate AgentCore deploy into TF
Roll our previously-manual container image build & deploy steps into Terraform, now that AgentCore is GA and Terraform supports it.
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‎README.md‎

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‎agentcore_runtime_deployment.ipynb‎

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‎chat_to_agentcore.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "71db7e7e",
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"metadata": {},
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"source": [
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"# Test your agent from a Python Notebook\n",
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"\n",
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"This interactive notebook provides an alternative way to test your deployed agent, besides the Streamlit UI app."
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]
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},
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{
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"cell_type": "markdown",
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"id": "cbced266",
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"metadata": {},
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"source": [
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"## Kernel selection and prerequisites\n",
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"\n",
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"You can use the `cx-agent-backend/.venv` as a kernel. If this is not set up already, run the following from your terminal:\n",
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"\n",
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"```bash\n",
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"cd cx-agent-backend\n",
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"uv venv\n",
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"uv sync --all-extras --frozen\n",
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"```\n",
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"\n",
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"This notebook assumes:\n",
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"1. You've already deployed the main solution as described in [README.md](./README.md)\n",
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"2. Your Python kernel is already configured with AWS credentials and target AWS Region (for example via environment variables, potentially set via a `.env` file as documented [here for VSCode](https://code.visualstudio.com/docs/python/environments#_environment-variables)).\n",
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" - Note that the AWS SDK for Python, `boto3`, [expects](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/configuration.html#using-environment-variables) an `AWS_DEFAULT_REGION` environment variable rather than `AWS_REGION`."
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]
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},
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{
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"cell_type": "markdown",
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"id": "b51a5349",
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"metadata": {},
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"source": [
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"## Dependencies and setup\n",
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"\n",
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"First we'll import the necessary libraries, and initialize clients for AWS Services, and define some utility functions that'll be used later:"
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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": "93b97307",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Python Built-Ins:\n",
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"import base64\n",
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"import json\n",
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"import getpass\n",
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"import hashlib\n",
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"import hmac\n",
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"import os\n",
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"import uuid\n",
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"import secrets\n",
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"import string\n",
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"import urllib.parse\n",
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"\n",
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"# External Libraries:\n",
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"import boto3 # AWS SDK for Python\n",
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"import requests # For making raw HTTP(S) API calls\n",
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"\n",
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"# AWS Service Clients:\n",
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"botosess = boto3.Session() # You could set `region_name` here explicitly if wanted\n",
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"cognito_client = botosess.client(\"cognito-idp\") # Cognito (Identity Provider)\n",
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"\n",
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"\n",
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"def _set_if_undefined(var: str, name: str | None = None) -> str:\n",
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" \"\"\"Utility to prompt user once for a value, and cache it in environment variable\"\"\"\n",
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" if not os.environ.get(var):\n",
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" os.environ[var] = getpass.getpass(f\"Please provide your {name or var}:\")\n",
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" return os.environ[var]\n",
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"\n",
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"\n",
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"def calculate_secret_hash(username, client_id, client_secret):\n",
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" \"\"\"Utility to hash a username + client ID + client secret for Cognito login\"\"\"\n",
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" message = username + client_id\n",
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" return base64.b64encode(\n",
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" hmac.new(\n",
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" client_secret.encode(\"utf-8\"),\n",
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" message.encode(\"utf-8\"),\n",
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" hashlib.sha256\n",
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" ).digest()\n",
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" ).decode(\"utf-8\")\n",
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"\n",
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"\n",
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"def invoke_agent(\n",
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" message,\n",
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" agent_arn,\n",
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" auth_token,\n",
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" session_id,\n",
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" qualifier=\"DEFAULT\",\n",
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" region=botosess.region_name,\n",
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"):\n",
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" \"\"\"Invoke Bedrock AgentCore runtime with a message.\"\"\"\n",
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" escaped_agent_arn = urllib.parse.quote(agent_arn, safe='')\n",
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" response = requests.post(\n",
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" f\"https://bedrock-agentcore.{region}.amazonaws.com/runtimes/{escaped_agent_arn}/invocations?qualifier={qualifier}\",\n",
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" headers={\n",
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" \"Authorization\": f\"Bearer {auth_token}\",\n",
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" \"Content-Type\": \"application/json\",\n",
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" \"X-Amzn-Bedrock-AgentCore-Runtime-Session-Id\": session_id\n",
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" },\n",
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" data=json.dumps({\"input\": {\"prompt\": message, \"conversation_id\": session_id}}),\n",
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" timeout=61,\n",
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" )\n",
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" \n",
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" print(f\"Status Code: {response.status_code}\")\n",
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" \n",
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" if response.status_code == 200:\n",
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" return response.json()\n",
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" else:\n",
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" raise ValueError(f\"HTTP {response.status_code}: {response.text}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b1f027bf",
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"metadata": {},
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"source": [
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"## Fetch access token from Amazon Cognito\n",
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"\n",
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"To talk to the AgentCore-deployed agent, we'll need to log in to Amazon Cognito to fetch a session token.\n",
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"\n",
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"You'll need to fetch your Cognito user_pool_id and client_id in the cell below, which you can view by running the `terraform output` command in your terminal:"
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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": "09e8aa2d",
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"metadata": {},
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"outputs": [],
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"source": [
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"user_pool_id = TODO # E.g. run `terraform output -raw user_pool_id`\n",
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"client_id = TODO # E.g. run `terraform output -raw client_id`\n",
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"\n",
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"# From these we should be able to look up the client secret automatically:\n",
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"client_secret = cognito_client.describe_user_pool_client(\n",
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" UserPoolId=user_pool_id,\n",
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" ClientId=client_id\n",
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")[\"UserPoolClient\"][\"ClientSecret\"]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "66238615",
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"metadata": {},
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"source": [
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"The next cell will prompt you for your Cognito username (email address) and password, or re-use the existing one if you run the cell again without restarting the notebook:"
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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": "00938dba",
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"metadata": {},
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"outputs": [],
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"source": [
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"username = _set_if_undefined(\"COGNITO_USERNAME\", \"Cognito user name (email address)\")\n",
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"password = _set_if_undefined(\"COGNITO_PASSWORD\", \"Cognito password\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e00e6221",
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"metadata": {},
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"source": [
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"The deployment steps in [README.md](./README.md) guide you through setting up your Cognito user from the AWS CLI, but you could instead un-comment and run the below to achieve the same effect from Python:"
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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": "fde3f16b",
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"metadata": {},
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"outputs": [],
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"source": [
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"## Create a user (with temporary password)\n",
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"# create_user_resp = cognito_client.admin_create_user(\n",
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"# UserPoolId=user_pool_id,\n",
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"# Username=username,\n",
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"# # Temp password is randomized here because we'll never use it:\n",
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"# TemporaryPassword=\"\".join((\n",
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"# secrets.choice(\n",
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"# string.ascii_uppercase + string.ascii_lowercase + string.digits +\n",
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"# \"^$*.[]{}()?-'\\\"!@#%&/\\\\,><':;|_~`+=\"\n",
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"# ) for i in range(20)\n",
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"# )),\n",
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"# MessageAction=\"SUPPRESS\"\n",
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"# )\n",
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"# print(f\"User created: {create_user_resp['User']['Username']}\")\n",
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"\n",
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"## Override the password to the given value (permanently)\n",
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"# set_password_resp = cognito_client.admin_set_user_password(\n",
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"# UserPoolId=user_pool_id,\n",
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"# Username=username,\n",
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"# Password=password,\n",
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"# Permanent=True,\n",
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"# )\n",
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"# print(\"Password set successfully\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ac6d3aff",
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"metadata": {},
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"source": [
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"With the configuration set up, we're ready to request an access token from Cognito:"
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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": "33858093",
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"metadata": {},
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"outputs": [],
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"source": [
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"auth_resp = cognito_client.initiate_auth(\n",
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" ClientId=client_id,\n",
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" AuthFlow=\"USER_PASSWORD_AUTH\",\n",
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" AuthParameters={\n",
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" \"USERNAME\": username,\n",
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" \"PASSWORD\": password,\n",
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" \"SECRET_HASH\": calculate_secret_hash(username, client_id, client_secret),\n",
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" }\n",
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")\n",
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"\n",
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"access_token = auth_resp[\"AuthenticationResult\"][\"AccessToken\"]\n",
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"print(\"Access token fetched\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "05e0c2e6",
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"metadata": {},
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"source": [
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"## Invoke the agent\n",
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"\n",
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"With the access token ready, we're almost ready to invoke our AgentCore Agent. First though, you'll need to:\n",
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"1. Look up the deployed AgentRuntime ARN from the terraform, and\n",
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"2. Choose a session ID (we'll randomize this automatically)"
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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": "6754ec78",
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"metadata": {},
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"outputs": [],
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"source": [
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"agent_arn = TODO # E.g. run `terraform output -raw agent_runtime_arn`\n",
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"\n",
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"session_id = str(uuid.uuid4()) # Can auto-generate this"
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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": "9cf235a1",
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"metadata": {},
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"outputs": [],
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"source": [
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"result = invoke_agent(\"Hello, can you help me resetting my router?\", agent_arn, access_token, session_id)\n",
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"if result:\n",
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" print(json.dumps(result, indent=2))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "583c6b54",
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"metadata": {},
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"source": [
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"### Testing math functionality"
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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": "60914206",
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"metadata": {},
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"outputs": [],
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"source": [
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"math_test = \"What is 25 + 17?\"\n",
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"print(f\"Testing math with: {math_test}\")\n",
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"result = invoke_agent(math_test, agent_arn, access_token, session_id)\n",
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"if result:\n",
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" print(json.dumps(result, indent=2))\n",
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"print(\"\\n\" + \"=\"*50 + \"\\n\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ceac8013",
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"metadata": {},
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"source": [
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"### Testing memory persistence"
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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": "68de9444",
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"metadata": {},
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"outputs": [],
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"source": [
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"message = \"Add 10 to the result\"\n",
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"print(message)\n",
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"result = invoke_agent(message, agent_arn, access_token, session_id)\n",
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"if result:\n",
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" print(json.dumps(result, indent=2))\n",
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"print(\"\\n\" + \"=\"*50 + \"\\n\")"
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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": "5caf85c9",
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"metadata": {},
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"outputs": [],
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"source": [
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"result = invoke_agent(\"What device did I want to reset?\", agent_arn, access_token, session_id)\n",
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"if result:\n",
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" print(json.dumps(result, indent=2))"
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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": "646cef68",
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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": ".venv",
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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": "3.12.8"
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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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