xatkit VS fastapi

While we wait for these tools to pop up, any tech question on the internals of Xatkit you'd like to know? And if you want to read more about the technologies we have listed above, this twitter thread gives some pointers to good tutorials for them:

(obviously) Create your own chatbots (pairing it up with Xatkit or any other chatbot platform for all the front-end and behaviour processing components)

The easiest part is to create the chatbot. We'll obviously use Xatkit for this. The bot can have as many intents as you wish. The only part that we care about here is the default fallback state. Here, instead of saying something useless, e.g. "sorry I didn't get your question, can you rephrase it and try again?", we will ask Haystack to find us a solution.

Take a look at Xatkit (https://github.com/xatkit-bot-platform/xatkit). It's an open source chatbot development platform and very easy to deploy on your own premises as the bot is compiled into a single .jar.

But, IMHO, many business owners do not really want to create a bot by themselves, no matter how easy is the chatbot development interface. They want to give you the data (whatever type of data they already have, e.g. an excel file with collected questions) and get a bot out of it. This is way at Xatkit we're now providing this type of "chatbot automatic generation services"

There are ML libraries available for every language. So there is always a way to execute/train your neural networks outside the python world. For instance, in Xatkit, we reuse Stanfords' Core NLP models in some of our language processors. And, if needed, there is always the option to wrap the ML models code in a Python server (I like the simplicity of Flask for this) and consume them via API calls to this server.

We have recently launched Xatkit, a pretrained chatbot for eCommerce. The reception so far has been lukewarm and we wonder whether:

Interested to give it a try? For FREE during the next two months? Visit: https://xatkit.com/ (and pls redistribute to your colleagues if you know anybody that could be interested, thanks!)

But this doesn't mean your no-code tool needs to stick to one specific category. As we do in Xatkit, you can offer different interfaces/importers on top of the same engine. You can even offer a low-code version for advanced users willing to use your tool's API to complement the result of the no-code approach.

He is probably most well know for creating FastAPI that I taught to some of my clients and Typer that I've never used.

It has been an interesting exercise developing this wrapper component. The fact that it seamlessly integrates with the FastAPI framework is just a bonus for me; I didn't plan for it since I hadn't learned FastAPI at the time. I hope you find this post useful. Thank you for reading, and stay safe as always.

FastAPI is a modern, high-performance web framework for building APIs with Python, based on standard Python type hints.

In this tutorial, I will demonstrate how to use Burr, an open source framework (disclosure: I helped create it), using simple OpenAI client calls to GPT4, and FastAPI to create a custom email assistant agent. We’ll describe the challenge one faces and then how you can solve for them. For the application frontend we provide a reference implementation but won’t dive into details for it.

That’s when I found FastAPI.

FastAPI & Uvicorn

Discussion at FastAPI GitHub: https://github.com/tiangolo/fastapi/issues/2696

An API application framework (such as FastAPI)

It’s been my experience that async Python frameworks tend to turn IO bound problems into CPU bound problems with a high enough request rate, because due to their nature they act as unbounded queues.

This ends up made worse if you’re using sync routes.

If you’re constrained on a resource such as a database connection pool, your framework will continue to pull http requests off the wire that a sane client will cancel and retry due to timeouts because it takes too long to get a connection out of the pool. Since there isn’t a straightforward way to cancel the execution of a route handler in every Python http framework I’ve seen exhibit this problem, the problem quickly snowballs.

This is an issue with fastapi, too- https://github.com/tiangolo/fastapi/issues/5759

Fast API.