and you can also select and use Gemini, Mistral, and LLaMA as a generative model.

Out-of-the-box data sources include Local, Google Cloud Storage, Google Drive, Slack, Jira, making it easy to create PoCs for a wide range of use cases.

For example, you can select and use GCP's { RagManagedDb, Vector Search, Feature Store } or third-party { Weaviate, pinecone } as underlying DB. In addition, you can select GCP's { text-embedding, gecko } or the open source model { e5-base | large | small } as an embedding model,

You can configure the desired RAG pipeline with various combinations, and you can also use the backend service developed and provided by Google.

blog on Hugging Face Daily Papers that is updated on a daily basis
: deep-diver.github.io/ai-paper-rev...

core project (paper-reviewer)
: github.com/deep-diver/p...

Please give ⭐️ to reach 700 on GitHub!!

I share these kinda contents that I actually build myself with collaborators.

If you are curious and want to know what's coming, please follow me!

Cheers 🍻

And this project got 550 @github.com 🌟 in a month. Notably, it comes with audio podcast for every papers whose quality is quite comparable to NotebookLM.

github.com/deep-diver/p...

My first ever full paper in the field of AI. This is quite unique exp since I am not ML background at all.

arxiv.org/abs/2408.13467