Advanced RAG: Chunking, Embeddings, and Vector Databases 🚀 | LLMOps

In this talk, Yujian from Zilliz talked about advanced RAG concepts including Chunking, Embeddings, and Vector Databases in RAG (Retrieval Augmented Generation) models
Topics that were covered:
✅ Chunking: Understand the concept of chunking and its role in improving the efficiency of information retrieval. Learn how to implement chunking in RAG to optimize the retrieval of relevant information.
✅ Embeddings: Dive into the world of embeddings, a method used to represent text as vectors. Discover how to enhance the performance of RAG models by enabling more accurate and efficient information retrieval.
✅ Vector Databases: Explore the use of vector databases in storing and managing embeddings. Learn how to leverage vector databases to speed up the retrieval process in RAG models.
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[@SteveMayzak]

You mention you’re the only distributed vector db. Is that true? There are multiple distributed vector dbs including Elasticsearch. What exactly makes you the only one?

[@darkreaper4990]

it's just marketing or they are living under a rock lol

[@micbab-vg2mu]

Thank you for the video.

[@gangs0846]

Thank you

[@faraazkhan]

Can i use different embedding models for chunk embedding and query embedding

[@karansingh-fk4gh]

Worst