the AI-native open-source embedding database
External link for Chroma
San Francisco, CA, US
Chroma reposted this
Database execution engines are an incredibly fun theoretical computer science and practical engineering problem to work on. I wrote about how Sanket Kedia Sicheng Pan and I designed Chroma's execution engine. https://lnkd.in/gzprXKBF
We're building a horizontally scalable, cloud-native distributed system, designed from the ground up to power retrieval for AI application workloads. Designing a serverless retrieval system is challenging; there are many trade-offs and constraints unique to retrieval for AI applications which must be carefully considered. In particular, retrieval workloads for AI applications differ significantly from traditional search workloads. Everything must be designed with the requirements of AI applications in mind. Learn about how we're using object storage to tackle this challenge here: https://lnkd.in/gu3Sa8hv
Introducing AI Dev Explainer (https://lnkd.in/gRjirVUt) - The best resource for getting started with building AI applications with LLM. We built this explainer because the most important thing anyone can be doing in AI today is onboarding more developers onto building with it. AI is great but it has often been marketed remarkably poorly. 'Generative AI' makes it sound like something that writes college essays, and most AI discussion makes it seem like you need a PhD. to understand it. But it's just an API anyone can use. We looked around at what's around on the web and found that resources are very fragmented, used a lot of jargon, and mainly focused on on people already building with AI. Our aim is to get the next million developers started. Part one is out now. Part two is coming soon. We welcome feedback, and will be iterating on this constantly and adding more topics, in response to the community. Share with your developer friends just getting started in AI!
NEW: Rahul Sonwalkar on building Julius AI https://lnkd.in/g2QJxWJC
we're hiring a community lead at chroma if you are passionate about open-source and accelerating AI engineering, apply! https://lnkd.in/dBePEexU
Embedding Adapters Today, we are pleased to share the first of a series of technical reports with the AI application developer community—our investigation into the use of linear embedding adapters in improving retrieval accuracy in realistic settings. Retrieval accuracy is an important determinant of AI application performance. However, many approaches to improving retrieval accuracy require large labeled corpora, which are often not available to application developers. Additionally, many of these approaches require re-computing the entire set of embeddings. While embedding adapters aren't a new idea, but to our knowledge this is the first time they have been investigated in depth. In this work, we demonstrate that applying a linear transform, trained from relatively few labeled data points, to just the query embedding, produces a significant (up to 70%) improvement in retrieval accuracy across many domains, including across languages. For many applications, this is the difference between working or not. Learn more: https://lnkd.in/gnC_FTFm
We are hiring for a founding talent / recruiter
[Hiring for a Founding Talent/Recruiter!] Chroma is nothing without it's people. We take hiring very seriously because our team defines what’s possible for us as a company. Candidates routinely tell us that they are impressed by the depth, speed, and quality of our interview process. A recent candidate: "that was by far the best onsite experience I've ever had" We are looking for the right person to found our talent team and take us to the next level. https://lnkd.in/g24E7Pbf please reach out directly at careers@trychroma.com [please comment/share so others in-market see this]