Building A Retrieval-Augmented Generation (RAG) Application Using Snowflake Cortex

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  • Опубликовано: 8 ноя 2024

Комментарии • 9

  • @Praveen_Kumar_R_CBE
    @Praveen_Kumar_R_CBE Месяц назад +2

    very good demo, thanks for sharing your knowledge

  • @Foodiecausehungry
    @Foodiecausehungry 16 дней назад +1

    Hey Brother can you also create a small tutorial like this using "Cortex Search Service" please?🥺

  • @satishkumaranamala2861
    @satishkumaranamala2861 20 дней назад

    "Thank you for your detailed explanation. I appreciate your efforts, and I would like to hear more details if possible. And suggest us how can we communicate with you.. I want to talk..

  • @Foodiecausehungry
    @Foodiecausehungry Месяц назад +3

    Which video are you reffering to can you tell me or provide me the name ? Please🥺

    • @KnowledgeAmplifier1
      @KnowledgeAmplifier1  Месяц назад +2

      All the prerequisite videos are available in the description section @Foodiecausehungry

  • @venugopal-nc3nz
    @venugopal-nc3nz Месяц назад +2

    Will u recommend learning snowflake or databricks ?

    • @KnowledgeAmplifier1
      @KnowledgeAmplifier1  Месяц назад +1

      @venugopal-nc3nz, both Snowflake and Databricks have their strengths, and the choice depends on your specific use case. If you're someone who enjoys working with SQL and wants a platform that simplifies data management, Snowflake is a fantastic option. It's incredibly easy to use, and with Snowflake’s latest additions (Snowpark), even those familiar with PySpark will find it versatile. Plus, if you're diving into machine learning or LLMs, Snowflake allows you to do this directly with SQL using Snowflake Cortex functions-no need for complex setups, making the process much smoother. So personally I have preference on Snowflake ..

    • @venugopal-nc3nz
      @venugopal-nc3nz Месяц назад +1

      @@KnowledgeAmplifier1 Thanks for helping me out