reframe VS Weaviate

NNext.ai » Software Engineer » Python » Full-time » NY, NY, USA » Remote

At [NNext.ai](http://NNext.ai) (read “Next.ai”), we are building building core infrastructure to simplify the process of building, deploying, and managing LLM-powered AI agents on tabular data, making it the most efficient solution available. NNext AI enables users to combine the power of Large Language Models (LLMs) like GPT with tools like the browser and Google Search, greatly increasing your productivity on workflows you once spent countless hours on. We are building a framework specialized for tabular data such as Spread sheets, Database Tables and CSVs, thereby taking advantage of the similarities and co-dependencies amongst data.

We are lean team of engineers looking for our first full time OSS contributor. Desirable experience includes distributed systems working with tools like Celery, Dash and Redis Streams. Visibility on Github is a huge plus. Show us previous OSS software you’ve contributed to or public personal projects with significant usage.

Website: https://nnext.ai

Github: https://github.com/nnextai/aigent

Email: [email protected]

https://github.com/nnextdb/nnext/blob/main/FAQs.md Q: How does this differ from Faiss, ScaNN and Annoy?

What’s NNext? [Say “Next”] NNextDB is a blazingly fast ⚡️, open-source 📖, (vector) neural search 🔎 engine for building delightful AI Apps 🦾. Github: https://github.com/nnextdb/nnext Like what you see? Please give us a Star 🤩. Applications Vector search is a key concept in modern machine learning systems. It’s the technology behind

These things mostly work by having some vision model, chopping it off somewhere in the middle and using that layer as an "embedding" for your image. Then you look for images that have embeddings which are close together (normally in the sense of Euclidean distance, e.g.)

Code Link: https://github.com/nnextdb/nnext

NNextDB - a blazing fast, open source, vector search database for building AP applications.

https://github.com/nnextdb/nnext

In this tutorial, I introduce Weaviate, an open-source vector database, with the thenlper/gte-base embedding model from Alibaba, through Hugging Face's transformers library.

This will be solved in Weaviate https://github.com/weaviate/weaviate/issues/2424

Link to GitHub -->

Hey @rvrs, I work on Weaviate and we are doing some improvements around increasing write throughput:

1. gRPC. Using gRPC to write vectors has had a really nice performance boost. It is released in Weaviate core but here is still some work on do on the clients. Feel free to get in contact if you would like to try it out.

2. Parameter tuning. lowering `efConstruction` can speed up imports.

3. We are also working on async indexing https://github.com/weaviate/weaviate/issues/3463 which will further speed things up.

In comparison with pgvector, Weaviate has more flexible query options such as hybrid search and quantization to save memory on larger datasets.