Embeddings and features for a better system

MLOps.community · Intermediate ·🔍 RAG & Vector Search ·6mo ago

Key Takeaways

The video discusses the importance of embeddings and features in building a better system, specifically in the context of RAG search, with a focus on Chrono's native embedding support and the potential for combining features and embeddings in end-to-end workflows

Full Transcript

And that intent is captured as embeddings, >> right? So what you're trying to do is essentially like take this intent side and match it with the policy side, right? There's two embeddings and you're doing a dot product to like figure out what's the most relevant information that LLM needs to get to make a decision. >> Fascinating. So we were on both sides of this but mostly on the user side because that's harder because you need to move a lot of real-time data embed it and store it in a vector index and then surface it. >> Yeah. >> Yeah. And so this is like something that's has some recent momentum in the Cronon open uh like first like native embedding support within Cronon. So >> it's not really features anymore. as like features and embeddings now which is pretty exciting and there's a ton of value in uh like orchestrating more complex graphs of you know combining features and embeddings together into endtoend workflows >> which again was pretty much prohibitively difficult before for all but the biggest most sophisticated teams >> but we're trying to make that just as easy as anything else in Chrono
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The video teaches how to use embeddings and features to improve RAG search systems, with a focus on Chrono's native embedding support and the potential for combining features and embeddings in end-to-end workflows. This is important because it allows for more complex and accurate search systems. By watching this video, viewers can learn how to implement RAG search with embeddings and features, and how to use Chrono's native embedding support to improve their search systems.

Key Takeaways
  1. Capture intent as embeddings
  2. Match intent embeddings with policy embeddings using dot product
  3. Store real-time data in a vector index
  4. Surface relevant information using embeddings and features
  5. Combine features and embeddings in end-to-end workflows
💡 The use of embeddings and features in RAG search can greatly improve the accuracy and complexity of search systems, and Chrono's native embedding support makes it easier to implement these systems

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