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RAG Document Search

Overview​

By leveraging a RAG pipeline to help users query a given knowledge base corpus, the Assistant can provide a more reliable and accurate knowledge base search experience. This not only enhances the overall user experience but also ensures that users receive the most relevant and up-to-date information possible by providing source links to the provided answers.

A RAG pipeline for Document Search usually consists of a Data Repository, a Vector Database and a Large Language Model. This pipeline can be carried out as one of three patterns.

Solution Implementation​

Method 1: watson Discovery​

This pattern consists of creating two integrations with Watson Discovery and watsonx.ai. Watson Discovery is used to store and carry out searches on data collections.

Required Integrations:

  • watson Discovery
  • watsonx.ai

RAG Method 1

Implementation Guide Here

Method 2: watsonx Discovery with Elasticsearch​

This pattern consists of creating an integration with watsonx Discovery. watsonx Discovery is used to store and carry out searches on data collections. Required Integrations:

  • watsonx Discovery
  • watsonx.ai

RAG Method 2

Implementation Guide Here