andre-z

What do I say? Happy to talk about "fakes". Here is my calendar. Feel free to book a slot. https://qdrant.to/andre-z

FAISS is not suitable for production. The dedicated vector search solutions solve all the issues you mentioned: you just store the metadata along with vectors in JSON format. At least, with Qdrant, it works like this: https://qdrant.tech/documentation/concepts/payload/

miniCOIL is a contextualized per-word embedding model. It generates extremely small embeddings (8dim or even 4dim) while still preserving the word's context for each word in a sentence.

GitHub https://github.com/qdrant/miniCOIL HuggingFace https://huggingface.co/Qdrant/minicoil-v1

"Slowness can arise from a misconfigured index or if filterable attributes aren't listed." ;)

Qdrant runs on Linux/Mac/Windows and on x86/ARM processors

Any dense embedding model can become a late interaction model.

Qdrant lib project https://github.com/tyrchen/qdrant-lib, Qdrant SDK has also support for local mode, which means embeddable https://github.com/qdrant/qdrant-client Or just use FAISS https://github.com/facebookresearch/faiss

The only other Repository is a fork of Qdrant.

I can answer how it would be in Qdrant if interested. The index will take around 70GB RAM. New vectors are first placed in a non-indexed segment and are immediately available for search while the index is being built. The vectors and the index can be offloaded to disk. Search will take some milliseconds.

Qdrant | Open Source Vector DB | Full-time | Remote/Berlin | https://qdrant.com

Hiring SRE, DevOps, Cloud Engineers, Rust Developers, and more.

https://qdrant.join.com

Thanks for your trust. :)

Yes. This makes sense. There is no criticism of Google at all. We just want to continue supporting contributors.

Sorry, my bad. Was partially copied from Linkedin, where I post most of the content. No idea behind. The emojis are from here https://qdrant.to/gsoc24

Did you look a bit deeper? Qdrant is written in Rust from scratch. The project is over three years old and started long before the "gold rush." :) Run it wherever you want, in your data center, locally, with no limitations. https://github.com/qdrant/qdrant-helm

This is not a reason. We participated successfully last year and even hired one of the guys afterward.

Qdrant | Open Source Vector DB | Remote | Full-time | https://qdrant.tech

SRE, Engineering Manager, DevOps, Cloud Engineer, Support Engineer, Rust Core Engineer (contribute first approach)

All positions: https://qdrant.join.com

Welcome to add on our Discord to #built with qdrant channel.

https://qdrant.to/discord

The open-source benchmarks show different results. Feel free to make a PR to improve. ;) https://qdrant.tech/benchmarks/

Thanks. We will reveal our hiring strategy in more details soon.

These are the major ones, correct.

Does this count? https://testimonial.to/qdrant/all

You should use whatever works best for you unless you face some limitations. The issue is that vector databases are not databases but search engines. It is ACID vs BASE. A few thoughts on this https://qdrant.tech/articles/dedicated-service/

Many: https://testimonial.to/qdrant/all

https://techcrunch.com/2024/01/23/qdrant-open-source-vector-...

Qdrant as a library project https://github.com/tyrchen/qdrant-lib

Qdrant | Rust Engineers, DevOps, Cloud Backend, SRE, ML Engineers, Sales, Marketing | Remote/Berlin/US | full-time | https://qdrant.tech

Qdrant is an open-source massive-scale Vector Database. https://github.com/qdrant/qdrant

All Positions: https://qdrant.join.com

Gemini is a new family of Google PaLM models, released in December 2023. The new embedding models succeed the previous Gecko Embedding Model.

See how to use new Gemini Embeddings with Qdrant Vector Database https://qdrant.tech/documentation/embeddings/gemini/

Qdrant is open source. You can run it on your locally. https://github.com/qdrant/qdrant

One from the Qdrant team here. We genuinely recommend starting with whatever you already have in your stack to prototype or produce applications with vector search. From the beginning, one should probably not start a new project with a complex micro-service architecture. The same as you can start with a full-text search using just Postgres or whatever you use as the main DB. However, as the requirements are getting higher and more complex, you probably should switch to a dedicated search engine to avoid the monolithic persistence anti-pattern. https://learn.microsoft.com/en-us/azure/architecture/antipat...

Regarding the dedicated solution, the difference is always in the details. That detail might significantly impact if you are looking for precision or working with large data. First of all, filtering the vector similarity search is quite tricky, and it does not work with pre and post-filtering because the graph's connectivity gets lost. That is why we introduced filterable HNSW at the very beginning. https://qdrant.tech/articles/filtrable-hnsw/ Here is another quite good explanation by James Briggs: https://www.pinecone.io/learn/vector-search-filtering/

Going further, it is not only about being able to search. It is also about being able to scale. Optionally, use disk for cold data as more affordable storage and keep hot data in expensive RAM. Or using one of the built-in compression functionalities. The recently introduced Binary Quantization makes it possible to compress vector embeddings up to 32 times and speed up the search up to 40 times in parallel. This makes billion-scale vector search affordable only for enterprise companies. https://qdrant.tech/articles/binary-quantization/

Native vector databases build all their features around vectors, and vectors are the first-class citizens in the architecture of the database core engine, not just another type of index support.

Vector search is not only about Text Search. See: https://qdrant.tech/articles/vector-similarity-beyond-search... It is also not even only about Search. Qdrant offers, for example, a dedicated Recommendation API, where you can submit negative and positive vector examples and get ready-made recommendation results back. https://qdrant.tech/articles/new-recommendation-api/ And the upcoming version of the engine will introduce even more functionalities like a new Discovery API, for example.

Ultimately, you do not need a vector database if you are looking for a simple vector search functionality. But you need one if you are looking to do more around it, and it is the central functionality of your application. It is just like using a multi-tool to make something quick or using a dedicated instrument highly optimized for the use case. Thank you for your attention.

https://github.com/qdrant/qdrant