How to Create a Medical Agent / RAG System.

Author(s): Pere Martra

Originally published on Towards AI.

Learn how to build a ReAct agent powered by a RAG system using LangChain, ChromaDB, and OpenAI, with a user-friendly Gradio interface.

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This article is based on content from my book “Large Language Models Projects: Apply and Implement Strategies for Large Language Models” (Apress) and a free GitHub course about LLMs.

As you’ve already read in the previous paragraph, this article is part of a Large Language Model course where concepts are introduced gradually. Here, we’ll see how to combine the technologies we’ve covered so far to create a small project.

If you’ve read any of my articles, you’ll know that their approach is very hands-on, and as expected, it’s accompanied by a notebook available in the course repository. You’ll find the link below:

Practical course about Large Language Models. . Contribute to peremartra/Large-Language-Model-Notebooks-Course…

github.com

My recommendation is to have the notebook open while reading the article and create your own version when you’re finished.

You’ll see how you can give LangChain the ability to use information from a database to feed the prompt, and you’ll also see how memory is maintained in conversations with these agents.

The agent you are going to create is of the ReAct type, which stands for Reasoning + Acting.

This means that the agent is programmed to perform… Read the full blog for free on Medium.

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Published via Towards AI