Building Our Nobel Prize KB Module

Welcome to the core development module!

In this lesson, we'll build a production-ready Nobel Prize Knowledge Base module using Naptha's KB architecture. You'll learn how to implement storage providers, query handlers, and data validation - essential skills for creating robust KB modules that can be used in real-world applications.

Understanding KB Architecture

Before we write any code, let's explore how our KB module works:

• Manages data through the Naptha Storage API
• Validates inputs using schema definitions
• Provides CRUD operations for Nobel Prize data
• Returns structured query responses

Building the Core Implementation

Let's break down our Nobel Prize KB implementation into its key components:

KB Module Initialization

The initialization sets up our KB with necessary configurations:

Data Population Implementation

The init method handles initial data loading:

Data Management Operations

The KB module provides comprehensive CRUD operations for managing Nobel Prize data allowing agent modules to add, read, update, and delete data:

Adding New Laureates

Listing Laureates

Deleting Data

The module supports both row-level and table-level deletions:

Query Implementation

The run_query method provides laureate search functionality:

Module Entry Point

The run function serves as our module's entry point:

Usage Examples

Here's how to test our KB module locally:

Error Handling Strategy

Our implementation follows a comprehensive error handling approach:

1. Schema-level validation (Pydantic)
2. Storage operation validation
3. Data integrity checks (e.g., duplicate prevention)
4. Detailed logging for debugging

Next Steps

In the next module, we'll explore:

• Testing our Nobel Prize KB module
• Deploying to Naptha Hub

Try experimenting with different queries and data operations before moving on. Understanding how the storage operations work will help when we move to deployment.