reframe VS qdrant

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

Great. Now that we have the embeddings, we need to store them in a vector database. We will be using Qdrant for this purpose. Qdrant is an open-source vector database that allows you to store and query high-dimensional vectors. The easiest way to get started with the Qdrant database is using the docker.

I took Qdrant for this project. The reason was that Qdrant stands for high-performance vector search, the best choice against use cases like finding similar function calls based on semantic similarity. Qdrant is not only powerful but also scalable to support a variety of advanced search features that are greatly useful to nuanced caching mechanisms like ours.

I'm currently looking to implement locally, using QDrant [1] for instance.

I'm just playing around, but it makes sense to have a runnable example for our users at work too :) [2].

[1]. https://qdrant.tech/

Also compare with qdrant's Rust implementation; they tout their performance. https://github.com/qdrant/qdrant/tree/master/lib/segment/src...

There are much better known examples, such as https://qdrant.tech/ and https://github.com/lancedb/lancedb

For more information, see our release notes. Qdrant is an open source project. We welcome your contributions; raise issues, or contribute via pull requests!

Initialize the Qdrant Client with in-memory storage. The collection name will be “imagebind_data” and we will be using cosine distance.

Qdrant is an open-source vector search engine optimized for performance and flexibility. It supports both exact and approximate nearest neighbor search, providing a balance between accuracy and speed for various AI and ML applications.