yujian

i've used this repo, it's a great starter pack

It's super interesting to be able to see the data in the web

tl;dr - less work = less pollution = less likely old people dying

Zilliz (zilliz.com) | Hybrid/ONSITE (SF, NYC) | Full-time

I am part of the hiring team for DevRel

NYC - https://boards.greenhouse.io/zilliz/jobs/4307910005

SF - https://boards.greenhouse.io/zilliz/jobs/4317590005

Zilliz is the company behind Milvus (https://github.com/milvus-io/milvus), the most starred vector database on GitHub. Milvus is a distributed vector database that shines in 1B+ vector use cases. Examples include autonomous driving, e-commerce, and drug discovery. (and, of course, RAG)

We are also hiring for other roles that I am not personally involved in the hiring process for such as product managers, software engineers, and recruiters.

oh yeah this is a great question, I get this a lot when I do my talks about RAG stuff

the way I see it is if you have a small amount of data (<10,000 vectors) then it's all the same and you should stick with the technology you are most familiar with

once you get more than that, you may want to consider a vector database

the reason that vector databases exist is because vector search is a highly compute intensive task, in regular database settings, you almost never have to run compute, the database is primarily looking to do an exact match

however, because vector search is predicated on the idea of finding similar vectors, and because exact vector matches are unlikely, you find yourself in the situation of having to optimize that

if you're building on a sql/nosql database you find yourself having to manage indexing, computing distance metrics, and load balancing

pgvector manages much of that for you, but due to the structure of SQL, it doesn't manage it in a very efficient manner - because it wasn't built to, an extra system needs to be built on top

as many experienced software engineers will tell you, adding complexity doesn't necessarily make something better, and adds more points of failure

purpose built vector databases like the ones in the article (eg milvus, chroma, weaviate) are built with this compute challenge in mind, and this becomes useful as the amount of data you have expands

Hi everyone, I put together this survey of tools for the LLM Stack in 2024. I've linked the friend-link for the Medium article in the URL. I'd love feedback from you guys about any tools I've missed.

If you're a Medium member and want to support my writing, feel free to use the regular link - https://medium.com/plain-simple-software/the-llm-app-stack-2...

Zilliz is hiring! We're looking for REMOTE and/or HYBRID roles in SF

Zilliz is the company behind Milvus (https://github.com/milvus-io/milvus), the most widely adopted vector database. Vector databases are a crucial piece of any technology stack looking to take advantage of unstructured data. Most recently and notably, Retrieval Augmented Generation (RAG). For RAG, vector databases like Milvus are used as the tool to inject customized data. In other words, vector databases make things like customized chat bots, personalized product recommendations, and more possible.

We are hiring for Developer Advocates, Senior+ Level Engineers and Product people, and Talent Acquisition. Check out all the roles here: https://zilliz.com/careers

Good on them, I know the crustaceans are out here happy about this raise for a Rust based Vector DB!

(now I'm gonna plug what I work on)

If you're interested in a more scalable vector database written in Go, check out Milvus (https://github.com/milvus-io/milvus)

missed the chance to call this "CeLLVM"

very nostalgic

I'm not sure if I'm missing something from the paper, but are multi-billion parameter models getting called "small" language models now? And when did this paradigm shift happen?

i'm also on dnd all day, i just don't wanna be disturbed

Anyscale consistently posts great projects. Very cool to see the cost comparison and quality comparison. Not surprising to see that OSS is less expensive, but also rated as slightly lower quality than gpt-3.5-turbo.

I do wonder, is there some bias in quality measures? Using GPT 4 to evaluate GPT 4's output? https://www.linkedin.com/feed/update/urn:li:activity:7103398...

this

Who's focusing on top 10 recall? I recently saw someone ask if we (I work at Zilliz) can update Milvus' recall to 10 million nearest neighbors lol