NeuML/neuml-linkedin-202501 huggingface dataset:
LinkedIn post by David Mezzetti (02/11/2024):
txtai is one of the easiest ways to get start with RAG. It has a vector database and LLM framework built-in. 

While releases are frequent, we don't have a reckless release cycle that constantly changes/breaks things. There is a focus on consistency and stability which is perhaps a bit more old fashioned.

https://lnkd.in/e8nfE-Zp
LinkedIn post by David Mezzetti (01/31/2024):
🚀 Coming with the next release of txtai: PEFT training support! 

txtai has long had a trainer pipeline that's designed for ease-of-use. Now we can simply set quantize=True and/or lora=True to automatically train a QLoRA or LoftQ model. Of course, the settings are also fully customizable. 

https://lnkd.in/dsZrtTiV
LinkedIn post by David Mezzetti (01/25/2024):
Typical solutions for multi-tenancy store all data in the same database. Access controls are then applied either in code or the database to control what users see. There is growing interest in evaluating if SQLite or similar can be a multi-tenant solution. In other words, every user or account has it's own database.

With txtai, a similar approach could be used for multi-tenancy. An embeddings database per account or user. In this setup, each embeddings database would be stored on a filesystem or cloud storage like S3.

Some would say this is CRAZY🤪 talk. But it's a setup that could potentially work in some situations. 

Read more here: https://lnkd.in/eMGY7uRB
LinkedIn post by David Mezzetti (01/25/2024):
🚀 Great to see that LlamaIndex has provided a txtai integration with their latest release!

While there are overlaps between the projects, it's always great to see two popular open source projects work together. OSS for the win. 

LlamaIndex: https://www.llamaindex.ai/
TxtaiVectorStore: https://lnkd.in/e8R2N5NY
LinkedIn post by David Mezzetti (01/20/2024):
NeuML ❤️'s Hugging Face. We have a growing number of models available on the hub. For example, given that txtai is a fully encapsulated index format, we have ArXiv and Wikipedia txtai embeddings databases available. These vector databases can be used directly or as a source for RAG. There's also models for generating medical literature embeddings, text to sql, text to speech and more. Check it out!

https://lnkd.in/eJTK9NRr
LinkedIn post by David Mezzetti (01/16/2024):
Excited to add a new knowledge base for RAG and more. Check out the txtai-arxiv embeddings index on the HF Hub!

The arXiv index works well as a fact-based context source for retrieval augmented generation (RAG). Additionally, this model can identify articles to cite in research. Passing a title + abstract pair will find similar existing articles.

Many of us look at arXiv for CS papers but it has much more. Articles on sports analytics, astronomy, physics and even the search for ET. Happy exploring!

https://lnkd.in/eSCCs-Jz
LinkedIn post by David Mezzetti (01/14/2024):
💭 txtchat is our solution for "quick up and running" self-hosted RAG applications. But it's much more than that! 

Given that integrates with Rocket Chat, you get user management, security, compliance and a mobile app. txtchat can also be easily extended to integrate with hosted solutions such as Slack and Teams. 

There are a lot of "toy" RAG apps built using Streamlit and/or a custom web framework. We all know that is far from a production solution, especially for an enterprise company.

Read more here: https://lnkd.in/eMzMMtTV