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05 GenAI on Databricks: Self-Managed Vector Search Index
A detailed technical guide for implementing Vector Search indexes in Databricks to enable retrieval-augmented generation (RAG) workflows, covering both self-managed and managed approaches with practic
al code examples. The article walks through the complete process from setup to testing, including similarity search implementation and result re-ranking.

Reasons to Read -- Learn:

  • how to implement a complete RAG system in Databricks using Vector Search, with step-by-step instructions and working code examples for both self-managed and managed indexes.
  • how to enhance search results in Databricks using the flashrank library for re-ranking, which can significantly improve the relevance of retrieved documents in your RAG applications.
  • practical techniques for connecting Delta tables with Vector Search endpoints and performing similarity searches, skills that are essential for building production-ready document retrieval systems.
  • publisher: @infinitylearnings1201
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