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Building Agent Modules with Knowledge Base Integration

Welcome to our first lesson on building intelligent multi-agent systems! In this module, we'll create an agent that leverages Knowledge Base modules to provide accurate, data-driven responses.

The complete implementation, including deployment configs and schemas, is available in our Nobel Prize Agent repository.

Before proceeding, make sure you have your environment set up: i.e cp .env.example .env and poetry install

Understanding Agent-KB Architecture

Let's explore how Naptha enables seamless integration between agents and knowledge bases:

This initialization shows how agents can:

  • Connect to Knowledge Base modules
  • Access the inference provider
  • Load configuration settings

Building Core Agent Functionality

The heart of our agent is its ability to query the KB and use that information intelligently:

Notice how we combine structured KB data with LLM capabilities to generate accurate, contextual responses.

Error Handling and Best Practices

You can also implement robust error handling:

Complete Implementation

Here's the full implementation of our Nobel Prize Agent:

Running the Agent

You can run the agent locally using this example script which will be placed in the run.py file:

Next, run the following command:

Want to deploy your agent? Check out our detailed guides on testing and deployment in other courses. E.g. Knowledge Base Modules Course.

Next Steps

In the next lesson, we'll explore how to enhance an agent module with access to a tool module for real-time data gathering and processing.

Ready to move on? Click "Next" or use the keyboard shortcut:

  • Mac: ⌘ + Enter
  • Windows/Linux: Ctrl + Enter
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