Your first RAG

  • In your project, select the dataset that will be used as your corpus. It needs to have at least one column of text

  • Create a new embedding recipe

  • Give a name to your knowledge bank

  • Select the embedding model to use

  • In the settings of the Embedding recipe, select the column of text

  • Optionally, select one or several metadata columns. These columns will be displayed in the Sources section of the answer

  • Run the embedding recipe

  • Open the Knowledge Bank

  • You will now define a Retrieval-Augmented LLM

    • Select the underlying LLM that will be queried

    • Optionally, tune the advanced settings for the search in the vector store

  • Click the Test in Prompt Studio button for your new Retrieval-Augmented LLM

    • This will automatically open Prompt Studio and create a new prompt for you, with your Retrieval-Augmented LLM pre-selected

  • Ask your question

  • You will now receive an answer that feeds on info gathered from your corpus dataset, with Sources indicating how this answer was generated