gptcli VS OpenAI-DotNet

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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.

You can obtain an OpenAI API key by visiting the OpenAI website.

OpenAI Account: Register for an OpenAI account to utilize AI-powered features in your ToolJet applications. Sign up here.

KodiBot is a desktop app that enables users to run their own AI chat assistants locally and offline on Windows, Mac, and Linux operating systems. KodiBot is a standalone app and does not require an internet connection or additional dependencies to run local chat assistants. It supports both Llama.cpp compatible models and OpenAI API.

At this point, probably everyone has heard about OpenAI, GPT-4, Claude or any of the popular Large Language Models (LLMs). However, using these LLMs in a production environment can be expensive or nondeterministic regarding its results. I guess that is the downside of being good at everything; you could be better at performing one specific task. This is where HuggingFace can utilized. HuggingFace provides open-source AI and machine learning models that can easily be deployed on HuggingFace itself or third-party systems such as Amazon SageMaker or Azure ML.  You can interface with these deployments through an API and control the scaling of these models, which makes them perfectly suited for production environments. These models range in size but are generally small AI models that are good at doing one specific task. With capabilities to fine-tune these models, or use the pre-trained model for specific tasks, embedding them into various applications becomes more efficient, enhancing automation and performance. Combining these models can create new and intricate AI applications. In this case, by utilizing HuggingFace models, you wouldn’t have to depend on a production application on a third-party provider such as OpenAI or Google, ensuring a more targeted and customizable approach to deploying deep learning solutions in your operations.

ChatGPT: OpenAi OpenAI is a research organization focused on developing artificial intelligence in a safe and beneficial manner. ChatGPT, one of their notable creations, is a language generation model capable of producing human-like text based on the input it receives.