cuttlefish VS OpenAI-DotNet

I made something vaguely similar for your local terminal[0].

The idea is to give you a chat with an assistant that can use local tooling. Here it's Python for data analysis, I went more in the direction of "give it access to your terminal, so it can answer questions / do tasks on your local machine" which is something web-based options can't do right now.

I.e. ask it about your system details (processes, wifi) or to do things (configure something). Have it run the relevant commands, analyze the output, and respond either in natural language or i.e. a chart.

It's a really cool area of putting AI in a feedback loop (langchain-like), which I think is where the magic happens, and where we'll see much more happening in the future.

[0]:https://github.com/cube2222/cuttlefish

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.