How to Build an LLM Query Engine in 10 Minutes | LlamaIndex x Ray Crossover

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Join Jerry, co-founder and CEO of LlamaIndex, and Amog, Ray developer at Anyscale as they discuss key challenges with building and scaling LLM applications.

Then, follow along as Amog takes you through a code tutorial that you can try and read more about in their jointly-written blog!

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#llm #machinelearning #ray #deeplearning #distributedsystems #python
  1. Anyscale
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Комментарии
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Thank you, Jerry and Amog! Nice demo! Keep up the good work!

Andromeda_
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always great videos! Love this topic but without openai..I would love to see something created only using open source technology...

fabsync
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What advantages does this have over taking all your data sources, creating embeddings for all and storing in a vector store. Then a single query would give the top_k responses and a summarization of those will give an answer? I can see that the question explicitly defines the data sources, but struggle to see the utility in this.

davidwynter
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is there a place a beginner can learn about ray from scratch

zahabkhan
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Can a subsequent SFT and RTHF with different, additional or lesser contents change the character, improve, or degrade a GPT model? Can you modify a GPT model? How?

amparoconsuelo
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What’s is the difference ray and embedded chain

rayhanpatel