Building Healthcare Conversation Agent using Research Papers

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About this Accelerator

This accelerator is aimed at showing how we can use Retrieval Augmented Generation to build a conversational agent for Healthcare professionals. Healthcare professionals have to constantly keep abreast the latest research in not only their own specialization but also in complimentary fields as well. This means they have to constantly consume the latest research from trusted sources. While research papers are published at an alarming rate, it becomes necessary to filter out research which is robust and only trusted research should be used as the knowledge base for the Agent. As this agent's intended use is in healthcare, it is paramount that the agent operates with in the confines of the knowledge base without hallucinations.


With DataRobot, we will show how to use Predictive Modeling to identify trusted research and then build a knowledge base for the conversational agent using DataRobot's Generative AI offering


What you will learn  

In this notebook we will be illustrating the following;

  • use predictive models to classify text files
  • create a vector store out of research paper abstracts
  • use Retrieval Augmented Generation with Generative AI model
  • deploy said Generative AI model to the DataRobot platform
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Last update:
‎09-14-2023 11:01 AM
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