tensorstore VS txtai

shhh...don't tell them about Tensorflow, or tf.Tensor, or theano.tensor, or paddle.to_tensor or torch.Tensor or torch.Tensor or mindspore.Tensor or tensorstore or ... But in all seriousness your pitchforks might be a little late to the party here. Rightly or wrongly (large) segments of the machine learning community have adopted terminology to describe things (for your sake, I hope you have not seen what they did with the term 'deconvolution'). This is not a new phenomena either and has been happening for at least a decade and almost certainly longer. However, there are other libraries that use the array terminology np.array, mx.nd.array, chainer, jax.numpy.array so it is definitely not unanimous. Whether you think this is an egregious mis-representation of the nomenclature or not is irrelevant now as it has a pretty established use within the community. Language is an evolving untamed beast - don't get angry, get building!

There are tons of other ways to store vector data, one was just recently released - https://github.com/google/tensorstore

Continue reading | Github | Google Full Blog

Today we are introducing TensorStore, an open-source C++ and Python software library designed for storage and manipulation of n-dimensional data that:

txtai is an all-in-one embeddings database for semantic search, LLM orchestration and language model workflows.

Bootstrapping only works if you have the runway to do it and you don't feel the need to grow fast.

With NeuML (https://neuml.com), I've went the bootstrapping route. I've been able to build a fairly successful open source project (txtai 6K stars https://github.com/neuml/txtai) and a revenue positive company. It's a "live within your means" strategy.

VC funding can have a snowball effect where you need more and more. Then you're in the loop of needing funding rounds to survive. The hope is someday you're acquired or start turning a profit.

I would say both have their pros and cons. Not all ideas have the luxury of time.

I agree that in many cases people are puffing their feathers to try to be something they're not (at least not yet). Some believe in the fake it until you make it mentality.

With NeuML (https://neuml.com), the website is a simple HTML page. On social media, I'm honest about what NeuML is, that I'm in my 40s with a family and not striving to be the next Steve Jobs. I've been able to build a fairly successful open source project (txtai 6K stars https://github.com/neuml/txtai) and a revenue positive company. For me, authenticity and being genuine is most important. I would say that being genuine has been way more of an asset than liability.

I'll add txtai (https://github.com/neuml/txtai) to the list.

There is still plenty of room for innovation in this space. Just need to focus on the right projects that are innovating and not the ones (re)working on problems solved in 2020/2021.

txtai is an all-in-one embeddings database for semantic search, LLM orchestration and language model workflows.

Nice project! I've long used Tika for document parsing given it's maturity and wide number of formats supported. The XHTML output helps with chunking documents for RAG.

Here's a couple examples:

- https://neuml.hashnode.dev/build-rag-pipelines-with-txtai

- https://neuml.hashnode.dev/extract-text-from-documents

Disclaimer: I'm the primary author of txtai (https://github.com/neuml/txtai).

If you're interested in graphs + RAG and want an alternate approach, txtai has a semantic graph component.

https://neuml.hashnode.dev/introducing-the-semantic-graph

https://github.com/neuml/txtai

Disclaimer: I'm the primary author of txtai

fwiw this link looks interesting, everyone

https://github.com/neuml/txtai