Jeff Huber (@jeffreyhuber, Founder/CEO @trychroma) talks vector databases, data integration, RAG, Embedded models, and AI integration.

SHOW: 771

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SHOW NOTES:

Topic 1 - Welcome to the show. Tell us a little bit about your background, and what brought you to create Chroma.

Topic 2 - Our audience, like many out there, is learning AI as fast as they can. So, let’s start with a few basic concepts. What is a vector database, and why is it important to AI?

Topic 3 - Does this only help foundational models? What goes into the integration into a model, and can this be used with any model?

Topic 4 - Is RAG (Retrieval-Augmented Generation) possible without a vector database?

Topic 5 - What are the trade-offs between fine-tuning a model (adding the data in) vs. RAG (keeping the data external)?

Topic 6 - What else is needed to create an “AI Stack”?

Topic 7 - Let’s talk about Chroma. First off, there is the OSS project which has been a huge success. Over 3 million downloads and 9.5k Github stars and inclusion into some 10k plus projects. Tell everyone a little bit about Chroma and what makes it different. You recently also announced a major milestone, over one million instances running

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