now VS haystack

💡In this post we’re just building out a backend, and not touching on a frontend. To build your own low-code backend+frontend neural search solution, check out Jina NOW.

Check out Jina NOW for an end-to-end text-to-image search

try it out for yourself now: https://examples.jina.ai/now/. Or if you'd prefer to go through the repo and see the magic behind the scenes: https://github.com/jina-ai/now

Here's the link to try it out for yourself: https://now.jina.ai

Github Repo

And here's the open-source code for developer friends here in the community - https://github.com/jina-ai/now

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)