01_Generation/01_zero_shot_generation.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "id": "497d5095-1305-4435-8970-f7fc40e2635b",
   "metadata": {},
   "source": [
    "# Invoke Bedrock model using LangChain and a zero-shot prompt\n",
    "\n",
    "> *This notebook should work well with the **`Data Science 3.0`** kernel in SageMaker Studio*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "406280e0-6c82-48e7-af07-4c18282f1b9d",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "In this notebook we show how to use a LLM to generate an email response to a customer who provided negative feedback on the quality of customer service that they received from the support engineer. \n",
    "\n",
    "We will use Anthropic's Claude model provided by Bedrock in this example. We will use the Bedrock version that is integrated with [LangChain](https://python.langchain.com/docs/get_started/introduction.html). LangChain is a framework for developing applications powered by language models. The key aspects of this framework allow us to augment the Large Language Models by chaining together various components to create advanced use cases.\n",
    "\n",
    "In this notebook we will use the Bedrock API provided by LangChain. The prompt used in this example is called a zero-shot prompt because we are not providing any additional context other than the prompt.\n",
    "\n",
    "**Note:** *This notebook can be run within or outside of AWS environment*.\n",
    "\n",
    "#### Context\n",
    "In the previous example `00_generate_w_bedrock.ipynb`, we explored how to use Boto3 client to communicate with Amazon Bedrock API. In this notebook, we will try to add a bit more complexity to leverage the LangChain framework for the similar use case. We will explore the use of Amazon Bedrock integration within LangChain framework and how it could be used to generate text with the help of `PromptTemplate`.\n",
    "\n",
    "#### Pattern\n",
    "We will simply provide the LangChain implementation of Amazon Bedrock API with an input consisting of a task, an instruction and an input for the model under the hood to generate an output without providing any additional example. The purpose here is to demonstrate how the powerful LLMs easily understand the task at hand and generate compelling outputs.\n",
    "\n",
    "![](./images/bedrock_langchain.jpg)\n",
    "\n",
    "#### Useccase\n",
    "To demonstrate the generation capability of models in Amazon Bedrock, let's take the use case of email generation.\n",
    "\n",
    "#### Persona\n",
    "You are Bob a Customer Service Manager at AnyCompany and some of your customers are not happy with the customer service and are providing negative feedbacks on the service provided by customer support engineers. Now, you would like to respond to those customers humbly aplogizing for the poor service and regain trust. You need the help of an LLM to generate a bulk of emails for you which are human friendly and personalized to the customer's sentiment from previous email correspondence.\n",
    "\n",
    "#### Implementation\n",
    "To fulfill this use case, in this notebook we will show how to generate an email with a thank you note based on the customer's previous email. We will use the Amazon Titan Text Large model using the Amazon Bedrock LangChain integration. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de0361e7-168f-46a3-be55-7071c4f0500e",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "Before running the rest of this notebook, you'll need to run the cells below to (ensure necessary libraries are installed and) connect to Bedrock.\n",
    "\n",
    "For more details on how the setup works and ⚠️ **whether you might need to make any changes**, refer to the [Bedrock boto3 setup notebook](../00_Intro/bedrock_boto3_setup.ipynb) notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "378c827b-802f-4e4c-8a4f-ab945d68e089",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Make sure you ran `download-dependencies.sh` from the root of the repository first!\n",
    "%pip install --no-build-isolation --force-reinstall \\\n",
    "    ../dependencies/awscli-*-py3-none-any.whl \\\n",
    "    ../dependencies/boto3-*-py3-none-any.whl \\\n",
    "    ../dependencies/botocore-*-py3-none-any.whl\n",
    "\n",
    "%pip install --quiet langchain==0.0.249"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2716a73f-c374-4493-8c62-fa4b2e750ee6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "import sys\n",
    "\n",
    "import boto3\n",
    "\n",
    "module_path = \"..\"\n",
    "sys.path.append(os.path.abspath(module_path))\n",
    "from utils import bedrock, print_ww\n",
    "\n",
    "\n",
    "# ---- ⚠️ Un-comment and edit the below lines as needed for your AWS setup ⚠️ ----\n",
    "\n",
    "# os.environ[\"AWS_DEFAULT_REGION\"] = \"<REGION_NAME>\"  # E.g. \"us-east-1\"\n",
    "# os.environ[\"AWS_PROFILE\"] = \"<YOUR_PROFILE>\"\n",
    "# os.environ[\"BEDROCK_ASSUME_ROLE\"] = \"<YOUR_ROLE_ARN>\"  # E.g. \"arn:aws:...\"\n",
    "# os.environ[\"BEDROCK_ENDPOINT_URL\"] = \"<YOUR_ENDPOINT_URL>\"  # E.g. \"https://...\"\n",
    "\n",
    "\n",
    "boto3_bedrock = bedrock.get_bedrock_client(\n",
    "    assumed_role=os.environ.get(\"BEDROCK_ASSUME_ROLE\", None),\n",
    "    endpoint_url=os.environ.get(\"BEDROCK_ENDPOINT_URL\", None),\n",
    "    region=os.environ.get(\"AWS_DEFAULT_REGION\", None),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d306331-7d3d-4a4a-9896-0d6050f3b7bd",
   "metadata": {
    "tags": []
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   "source": [
    "## Invoke the Bedrock client using LangChain Integration\n",
    "\n",
    "Lets begin with creating an instance of Bedrock class from llms. This expects a `model_id` of the model available in Amazon Bedrock. \n",
    "\n",
    "Optionally you can pass on a previously created boto3 `client` as well as some `model_kwargs` which can hold parameters such as `temperature`, `topP`, `maxTokenCount` or `stopSequences` (more on parameters can be explored in Amazon Bedrock console).\n",
    "\n",
    "Available text generation models under Amazon Bedrock have the following IDs:\n",
    "\n",
    "- amazon.titan-tg1-large\n",
    "- ai21.j2-grande-instruct\n",
    "- ai21.j2-jumbo-instruct\n",
    "- anthropic.claude-instant-v1\n",
    "- anthropic.claude-v2\n",
    "\n",
    "Note that different models support different `model_kwargs`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13f75ea1-dce1-4794-84bf-68d9c22a2d97",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.llms.bedrock import Bedrock\n",
    "\n",
    "inference_modifier = {\n",
    "    \"max_tokens_to_sample\": 4096,\n",
    "    \"temperature\": 0.5,\n",
    "    \"top_k\": 250,\n",
    "    \"top_p\": 1,\n",
    "    \"stop_sequences\": [\"\\n\\nHuman\"],\n",
    "}\n",
    "\n",
    "textgen_llm = Bedrock(\n",
    "    model_id=\"anthropic.claude-v2\",\n",
    "    client=boto3_bedrock,\n",
    "    model_kwargs=inference_modifier,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f853488-8f26-4cbf-8c42-96daee8a9351",
   "metadata": {},
   "source": [
    "By passing the `client` in to LangChain, we should be able to ensure that the library uses the same boto3 client we checked the configuration of earlier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b35736d5-8ded-4132-8cec-e34b8437193b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "print(boto3_bedrock)\n",
    "print(textgen_llm.client)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9fc4301",
   "metadata": {},
   "source": [
    "LangChain has abstracted away the Amazon Bedrock API and made it easy to build use cases. You can pass in your prompt and it is automatically routed to the appropriate API to generate the response. You simply get the text output as-is and don't have to extract the results out of the response body.\n",
    "\n",
    "Let's prepare the prompt to generate an email for the Customer Service Manager to send to the customer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4e3304c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "response = textgen_llm(\"\"\"\n",
    "\n",
    "Human: Write an email from Bob, Customer Service Manager, \n",
    "to the customer \"John Doe\" that provided negative feedback on the service \n",
    "provided by our customer support engineer.\n",
    "\n",
    "Assistant:\"\"\")\n",
    "\n",
    "print_ww(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8ed23ea",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "You have now experimented with using `LangChain` framework which provides an abstraction layer on Amazon Bedrock API. Using this framework you have seen the usecase of generating an email responding to a customer due to their negative feedback.\n",
    "\n",
    "### Take aways\n",
    "- Adapt this notebook to experiment with different models available through Amazon Bedrock such as Anthropic Claude and AI21 Labs Jurassic models.\n",
    "- Change the prompts to your specific usecase and evaluate the output of different models.\n",
    "- Play with the different parameters to understand the latency and responsiveness of the service.\n",
    "- Apply different prompt engineering principles to get better outputs.\n",
    "\n",
    "## Thank You"
   ]
  }
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