06_CodeGeneration/02_code_interpret_w_langchain.ipynb

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   "source": [
    "# Bedrock with LangChain - Explain/Interpret a code snippet or program \n",
    "> *This notebook should work well with the **`Data Science 3.0`** kernel in SageMaker Studio*"
   ]
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    "## Introduction\n",
    "\n",
    "In this notebook we show you how to explain or interpret a given code snippet or program.\n",
    "\n",
    "[LangChain](https://python.langchain.com/docs/get_started/introduction.html) 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 creates a custom LangChain prompt template for adding context to the code explain request. \n",
    "\n",
    "**Note:** *This notebook can be run within or outside of AWS environment.*\n",
    "\n",
    "#### Context\n",
    "In the previous example `01_sql_query_generation_w_bedrock.ipynb`, we explored how to use Bedrock API. In this notebook we will try to add a bit more complexity with the help of `PromptTemplates` to leverage the LangChain framework. `PrompTemplates` allow you to create generic shells which can be populated with information later and get model outputs based on different scenarios.\n",
    "\n",
    "As part of this notebook we will explore the use of Amazon Bedrock integration within LangChain framework and how it could be used to generate or explain code 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/code-interpret-langchain.png)\n",
    "\n",
    "#### Use case\n",
    "To demonstrate the code generation capability of models in Amazon Bedrock, let's take the use case of code explain.\n",
    "\n",
    "#### Persona\n",
    "You are Joe, a Java software developer, has been tasked to support a legacy C++ application for Vehicle Fleet Management. You need help to explain or interpret certain complex C++ code snippets as you are performing analyis to identify the business logic and potential problems with the code.\n",
    "\n",
    "#### Implementation\n",
    "To fulfill this use case, we will show you how you can Amazon Bedrock API with LangChain to explain C++ code snippets.\n"
   ]
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   "cell_type": "markdown",
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   "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.\n",
    "\n",
    "In this notebook, we'll also install the [Hugging Face Transformers](https://huggingface.co/docs/transformers/index) library which we'll use for counting the number of tokens in an input prompt."
   ]
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   "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 \"transformers>=4.24,<5\""
   ]
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   "cell_type": "code",
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   "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",
    "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",
    ")"
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    "## Invoke the Bedrock LLM Model\n",
    "\n",
    "We'll begin with creating an instance of Bedrock class from llms. This expects a `model_id` which is the ARN 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-v1\n",
    "\n",
    "Note that different models support different `model_kwargs`."
   ]
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   "source": [
    "from langchain.llms.bedrock import Bedrock\n",
    "\n",
    "inference_modifier = {'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(model_id = \"anthropic.claude-v2\",\n",
    "                    client = boto3_bedrock, \n",
    "                    model_kwargs = inference_modifier \n",
    "                    )\n"
   ]
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   "source": [
    "## Create a LangChain custom prompt template\n",
    "\n",
    "By creating a template for the prompt we can pass it different input variables to it on every run. This is useful when you have to generate content with different input variables that you may be fetching from a database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "96bc21b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Vehicle Fleet Management Code written in C++\n",
    "sample_code = \"\"\"\n",
    "#include <iostream>\n",
    "#include <string>\n",
    "#include <vector>\n",
    "\n",
    "class Vehicle {\n",
    "protected:\n",
    "    std::string registrationNumber;\n",
    "    int milesTraveled;\n",
    "    int lastMaintenanceMile;\n",
    "\n",
    "public:\n",
    "    Vehicle(std::string regNum) : registrationNumber(regNum), milesTraveled(0), lastMaintenanceMile(0) {}\n",
    "\n",
    "    virtual void addMiles(int miles) {\n",
    "        milesTraveled += miles;\n",
    "    }\n",
    "\n",
    "    virtual void performMaintenance() {\n",
    "        lastMaintenanceMile = milesTraveled;\n",
    "        std::cout << \"Maintenance performed for vehicle: \" << registrationNumber << std::endl;\n",
    "    }\n",
    "\n",
    "    virtual void checkMaintenanceDue() {\n",
    "        if ((milesTraveled - lastMaintenanceMile) > 10000) {\n",
    "            std::cout << \"Vehicle: \" << registrationNumber << \" needs maintenance!\" << std::endl;\n",
    "        } else {\n",
    "            std::cout << \"No maintenance required for vehicle: \" << registrationNumber << std::endl;\n",
    "        }\n",
    "    }\n",
    "\n",
    "    virtual void displayDetails() = 0;\n",
    "\n",
    "    ~Vehicle() {\n",
    "        std::cout << \"Destructor for Vehicle\" << std::endl;\n",
    "    }\n",
    "};\n",
    "\n",
    "class Truck : public Vehicle {\n",
    "    int capacityInTons;\n",
    "\n",
    "public:\n",
    "    Truck(std::string regNum, int capacity) : Vehicle(regNum), capacityInTons(capacity) {}\n",
    "\n",
    "    void displayDetails() override {\n",
    "        std::cout << \"Truck with Registration Number: \" << registrationNumber << \", Capacity: \" << capacityInTons << \" tons.\" << std::endl;\n",
    "    }\n",
    "};\n",
    "\n",
    "class Car : public Vehicle {\n",
    "    std::string model;\n",
    "\n",
    "public:\n",
    "    Car(std::string regNum, std::string carModel) : Vehicle(regNum), model(carModel) {}\n",
    "\n",
    "    void displayDetails() override {\n",
    "        std::cout << \"Car with Registration Number: \" << registrationNumber << \", Model: \" << model << \".\" << std::endl;\n",
    "    }\n",
    "};\n",
    "\n",
    "int main() {\n",
    "    std::vector<Vehicle*> fleet;\n",
    "\n",
    "    fleet.push_back(new Truck(\"XYZ1234\", 20));\n",
    "    fleet.push_back(new Car(\"ABC9876\", \"Sedan\"));\n",
    "\n",
    "    for (auto vehicle : fleet) {\n",
    "        vehicle->displayDetails();\n",
    "        vehicle->addMiles(10500);\n",
    "        vehicle->checkMaintenanceDue();\n",
    "        vehicle->performMaintenance();\n",
    "        vehicle->checkMaintenanceDue();\n",
    "    }\n",
    "\n",
    "    for (auto vehicle : fleet) {\n",
    "        delete vehicle; \n",
    "    }\n",
    "\n",
    "    return 0;\n",
    "}\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dbec103a-97ae-4e9e-9d80-dc20f354a228",
   "metadata": {
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   "source": [
    "from langchain import PromptTemplate\n",
    "\n",
    "# Create a prompt template that has multiple input variables\n",
    "multi_var_prompt = PromptTemplate(\n",
    "    input_variables=[\"code\", \"programmingLanguage\"], \n",
    "    template=\"\"\"Human: You will be acting as an expert software developer in {programmingLanguage}. \n",
    "                You will explain below code and highlight if any red flags or not following best practices.\n",
    "                {code}\n",
    "                Assistant: \n",
    "            \"\"\"\n",
    ")\n",
    "\n",
    "# Pass in values to the input variables\n",
    "prompt = multi_var_prompt.format(code=sample_code, programmingLanguage=\"C++\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5b76387",
   "metadata": {},
   "source": [
    "### Explain C++ Code for Vehicle Fleet management using Amazon Bedrock and LangChain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1064c57-27a4-48c5-911b-e4f1dfeff122",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "response = textgen_llm(prompt)\n",
    "\n",
    "code_explanation = response[response.index('\\n')+1:]\n",
    "\n",
    "print_ww(code_explanation)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e9abc40",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "To conclude we learnt that invoking the LLM without any context might not yield the desired results. By adding context and further using the the prompt template to constrain the output from the LLM we are able to successfully get our desired output"
   ]
  }
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