Slack-GPT VS OpenAI-DotNet

If you know C#, I have written a Slack Bot to integrate ChatGPT with Slack. https://github.com/Prographers/Slack-GPT You can read more on how I wrote that and maybe use it for your own Slack workspace!

I want leave an alternative here, this is not PHP but maybe useful to someone! https://github.com/Prographers/Slack-GPT

Website: https://prographers.com/blog/enhance-your-slack-experience-with-the-chatgpt-slack-bot Github: https://github.com/Prographers/Slack-GPT

Github: https://github.com/Prographers/Slack-GPT Integrate your ChatGPT experience and collaborate with your team mates without leaving slack! By default using GPT-4!

Docker support Github: https://github.com/Prographers/Slack-GPT

Okay, now we've confirmed the API endpoint is working, let's connect it to OpenAI first, install the OpenAI package, navigate to the route directory, and run the command below in our terminal

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.