xatkit VS haystack

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.

I was confused for a bit but there is no relation to https://haystack.deepset.ai/

View on GitHub

4. Haystack by Deepset | Github | tutorial

Haystack can be classified as an end-to-end framework for building applications powered by various NLP technologies, including but not limited to generative AI. While it doesn't directly focus on building generative models from scratch, it provides a robust platform for:

But if you want an API that you can use to develop your own flow, Haystack from Deepset could be worth a look.

Haystack for production. We cannot afford breaking changes in our production apps. Its stable, documentation is excellent and did I mention its' STABLE!??

I recently conducted some experiments with Llama2 and Haystack (https://github.com/deepset-ai/haystack), the NLP/LLM framework.

The notebook can be helpful for those trying to load Llama2 on Colab.

1) Installed Transformers from the main branch (and other libraries)