gptcli VS askai

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For a long time, I was looking for something exciting to work on and this was it. The fact that ChatCraft already supported Speech to Text transcription using Whisper, which is another one of OpenAI's models with unique capabilities, integrating Text-to-Speech would essentially turn our application into something like an Amazon Alexa but with a brain powered the same LLM that ChatGPT uses.

With your API key, you’ll be able to make authenticated HTTP requests to OpenAI. So it’s a good idea to get familiar with the API itself. I’d encourage you to take a brief look through the OpenAI Documentation and become familiar with some concepts. The models are particularly good to understand because they have varying capabilities.

Next, we’ll also need an OpenAI account to have access to their API.

Capable of reasoning:The application can be connected to a large language model like OpenAI, Cohere, or Hugging Face. This enables the application to answer based on the context it is given.

Beyond that, "AI tools" is a pretty broad category. Odds are you're asking about LLMs (perhaps even specifically GPT), in which case a good place to start is with OpenAI and their API. Alternatively, Hugging Face is a good place to find models (not just LLMs, either) and they have tooling to let you interact with them in a consistent way. In these cases, you're not actually developing an AI, per se, but just utilizing it. But when I think of "AI Tools" at this point in time, I'm thinking of tools built on top of LLMs.

Like most things in IT and programming, this imo is also best learned by doing rather than reading a bunch of technical papers. I recommend starting with ChatGPT and testing out varieties of prompts asking it to fill in your knowledge. There are great documentation by both OpenAI and Microsoft Azure on using these models with examples. For open-source models (and transformers in general) HuggingFace is a great resource and you can get started on some smaller models by downloading it and trying right away. Karpathy's nanoGPT video on Youtube is also very useful to get started.

It's just a gui to (most of) what you get through the API. Read the API docs for details of each option: https://platform.openai.com/docs/introduction

For the full list of options, check out the OpenAI API documentation.

If you are interested in an API for ChatGPT in general, you can go look at the documentation or this starter guide for gpt-4 and gpt-3.5-turbo on the openai website. This should then be adapted to the language you are using.

This API allows you to interact with ChatGPT programmatically, and I've built some cool agents on top of it. Check out the code and let me know what you think! :

ChatGPT unofficial API :

This project is a Node.js application that interacts with the ChatGPT conversational AI model using Puppeteer, a Node.js library for automating web browsers.

Files chatgptv1.js: This file contains the main logic for the ChatGPT bot, including methods for initializing the browser, sending messages, receiving replies, and handling errors.

bart.js: This file contains a function that uses the Cloudflare API to summarize the conversation history when an error occurs, in order to resume the conversation.

twochatbotsconv.js: This file is simple use of the API , which creates two instances of the ChatGPT class, initiates a conversation between them, and saves the conversation history to a file.

.env: This file contains the API token for the Cloudflare API, which is used in the bart.js file.

Dependencies :

puppeteer: A Node.js library for automating web browsers. fs: The built-in file system module in Node.js. winston: A logging library for Node.js. crypto: The built-in cryptography module in Node.js. axios: A popular HTTP client library for Node.js. dotenv: A zero-dependency module that loads environment variables from a .env file.

Usage:

Install the dependencies by running npm install in your project directory. Create a .env file in the project directory and add your Cloudflare API token:

API_TOKEN=YourfreeCloudFlareAPIToken In your code, create a new instance of the ChatGPT class and use the sendMessage and getReply methods to interact with the ChatGPT model:

const ChatGPT = require('./chatgptv1');

const chatgpt = new ChatGPT(); await chatgpt.initializeBrowser();

await chatgpt.sendMessage('Hello, ChatGPT!'); const reply = await chatgpt.getReply(); console.log(reply);

await chatgpt.closeBrowser(); If an error occurs during the conversation, the handleError method will attempt to save the conversation history and resume the conversation using the summarized context.

Before Running :

run Google chrom in the debug mode using 9220 port , run : google-chrome-stable --remote-debugging-port=9222

Customization :

You can customize the behavior of the ChatGPT bot by passing options to the ChatGPT constructor:

chatbotUrl: The URL of the ChatGPT interface (default: 'https://chat.openai.com/'). headless: Whether to run the browser in headless mode (default: false). saveConversationCallback: A callback function that will be called with the conversation summary and the conversation file name when an error occurs.

License:

This project is licensed under the MIT License.

After wrestling with it for a bit and eventually giving up, I instead turned to ChatGPT to see if it is smart enough for the task. With my free plan, I asked ChatGPT 3.5 the following:

Go to chat.openai.com and log in

In this tutorial, we will learn how to use Strapi, ChatGPT, and Next.js to build an app that displays recipes using AI.