• What is this repo about?
• How to run the code
• Wanna get more hands-on content like this?

In this repository you will find a Python implementation of an LLM agent that can generate visualisations using public data available on the Internet.

An agent is essentially a wrapper around your LLM, that provides extra functionality like

• Tool usage. The LLM is able to select and use tools, like internet search, to fetch relevant information it might need to accomplish the task.

• Multi-step reasoning. The LLM can generate a plan, execute it, and adjust it based on the partial outcomes obtained.

The LLM acts as a reasoning machine, that helps the agent choose the sequence of actions to take to solve the task.

Let me show you how to build a ReAct (Reason and Act) agent in Python that can generate the plot we want.

1. Create Python virtual environment and install all dependencies using Python Poetry

   $ make install
   

2. Set API keys for Tavily and Cohere in an .env file

   $ cp .env.sample .env
   

   and replace placeholders with your keys.

3. Ask the agent to generate a plot example

   $ make run-agent
   

Jon 15k+ builders to the the Real-World ML Newsletter, and learn to build real-world ML products.

-> Click to join for FREE