DoctorEw's Lab Lambda Backend

Welcome to DoctorEw's Lab Lambda Backend! This project leverages AWS Lambda, LangChain, and OpenAI's GPT-4o model to provide various AI-driven endpoints. It includes chat functionalities, translation services, sentiment analysis, code generation, and more, with dynamic personalities.

Table of Contents

1. Introduction
2. Features
3. Installation
4. Running the Project
5. Project Structure
6. Understanding the Code
7. Customization
8. Troubleshooting

Introduction

This project is designed to help 8th graders understand the basics of serverless computing and AI integration using AWS Lambda and OpenAI's GPT-4 model. It includes multiple endpoints to interact with AI functionalities like chat, translation, sentiment analysis, and code generation.

Features

• Chatbot: Interact with various personalities.
• Translation: Translate text into different languages with personality-specific responses.
• Sentiment Analysis: Analyze the sentiment of a given text.
• Code Generation: Generate code snippets in different programming languages.
• Personal Assistant: Get reminders, to-do lists, or motivational quotes.

Installation

To get started with this project, you'll need to have Node.js and npm (Node Package Manager) or yarn installed on your computer. You can download Node.js from here.

1. Clone the Repository:

   git clone https://github.com/doctor-ew/drew_lab_vsk.git
   cd drew_lab_vsk

2. Install Dependencies:

   npm install

3. Set Up Environment Variables:

   Create a .env file in the root directory of the project and add your OpenAI API key:

   OPENAI_API_KEY=your_openai_api_key_here

Running the Project

To run the project locally, follow these steps:

1. Build the Project:

   npm run build

2. Start the Local Development Server:

   npm run dev

   This will start the local development server and you can test the Lambda functions using tools like Postman or Curl.

Project Structure

Here's a brief overview of the project's structure:

drew_lab_vsk/
├── config/
│   └── personalityMatrix.ts      # Personality templates
├── src/
│   ├── handlers/
│   │   └── chatbot.ts            # Chatbot handler
│   ├── index.ts                  # Main Lambda handler
│   ├── prompts/
│   │   └── templates.ts          # Prompt templates
│   └── utils/
│       └── langchainUtils.ts     # LangChain utility functions
├── .env                          # Environment variables
├── package.json                  # Project metadata and scripts
├── tsconfig.json                 # TypeScript configuration
└── README.md                     # Project documentation

Understanding the Code

Key Files and Directories

• config/personalityMatrix.ts: Contains personality templates for different characters.
• src/handlers/chatbot.ts: Handles the chatbot functionality, including fetching responses and updating the UI.
• src/prompts/templates.ts: Contains prompt templates for different functionalities (chat, translation, etc.).
• src/utils/langchainUtils.ts: Utility functions for LangChain operations.

Adding a New Personality

To add a new personality:

1. Update config/personalityMatrix.ts with the new personality's details.

Endpoints

The Lambda function supports the following endpoints:

• Chatbot: /chatbot
• Translation: /translation
• Personal Assistant: /personalAssistant
• Sentiment Analysis: /sentimentAnalysis
• Code Generation: /foxtrot

Example Lambda Handler

import { APIGatewayProxyEvent, APIGatewayProxyResult } from "aws-lambda";
import { getOpenAIResponse, getTranslation, getPersonalAssistantResponse, getSentimentAnalysis, getFoxtrotCode } from "./utils/langchainUtils";
import personalities from "./config/personalityMatrix";

export const handler = async (event: APIGatewayProxyEvent): Promise<APIGatewayProxyResult> => {
  try {
    const requestBody = JSON.parse(event.body || "{}");
    const userQuestion = requestBody.question || "What's up, Doc?";
    const targetLanguage = requestBody.language || "Spanish";
    const programmingLanguage = requestBody.programmingLanguage || "Python";
    const personality = requestBody.personality || "skippy"; // Default to Skippy

    let response;
    switch (event.path) {
      case "/chatbot":
        response = await getOpenAIResponse(userQuestion, personality);
        break;
      case "/translation":
        response = await getTranslation(userQuestion, targetLanguage);
        break;
      case "/personalAssistant":
        response = await getPersonalAssistantResponse(userQuestion);
        break;
      case "/sentimentAnalysis":
        response = await getSentimentAnalysis(userQuestion);
        break;
      case "/foxtrot":
        response = await getFoxtrotCode(programmingLanguage);
        break;
      default:
        response = "Unknown endpoint path.";
    }

    return {
      statusCode: 200,
      headers: {
        "Access-Control-Allow-Headers": "Content-Type",
        "Access-Control-Allow-Origin": "*",
        "Access-Control-Allow-Methods": "OPTIONS,POST",
      },
      body: JSON.stringify({ answer: response }),
    };
  } catch (error) {
    if (error instanceof Error) {
      return {
        statusCode: 500,
        headers: {
          "Access-Control-Allow-Headers": "Content-Type",
          "Access-Control-Allow-Origin": "*",
          "Access-Control-Allow-Methods": "OPTIONS,POST",
        },
        body: JSON.stringify({ error: error.message }),
      };
    }
    return {
      statusCode: 500,
      headers: {
        "Access-Control-Allow-Headers": "Content-Type",
        "Access-Control-Allow-Origin": "*",
        "Access-Control-Allow-Methods": "OPTIONS,POST",
      },
      body: JSON.stringify({ error: "An unknown error occurred" }),
    };
  }
};

Customization

You can customize the project to add more features or change the existing ones. Here are some ideas:

• Add more personalities and their corresponding templates.
• Modify the responses to include more detailed or varied information.
• Add more endpoints for different types of AI functionalities.

Troubleshooting

If you encounter any issues while running the project, try the following steps:

• Ensure you have the latest version of Node.js and npm installed.
• Check the AWS Lambda logs for any error messages and resolve them accordingly.
• Ensure your OpenAI API key is correctly set in the .env file.

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Feel free to reach out if you have any questions or need further assistance. Happy coding!

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This README was written by Bob (based off of Wizard Harry Dresden's sarcastic, ancient air-spirit assistant), and now my mystical ChatGPT 4o coding companion, to guide you through setting up and running the Skippy Chatbot project. Enjoy coding with a touch of sarcasm and wit!

To Install Node on Windows

Set-ExecutionPolicy Bypass -Scope Process -Force; [System.Net.ServicePointManager]::SecurityProtocol = [System.Net.ServicePointManager]::SecurityProtocol -bor 3072; iex ((New-Object System.Net.WebClient).DownloadString('https://community.chocolatey.org/install.ps1'))

choco -v

choco install nodejs-lts -y

node -v npm -v

npm install --save-dev @types/express @types/aws-lambda

npm run build npm run dev