Building an Expense Manager using GenAI| Exploring Pydantic, Tools, and Agents

Introduction

Managing expenses is often seen as a tedious task, involving scanning bills, categorizing expenditures, and calculating totals. What if we could automate this process using the power of GenAI? Enter the Expense Manager, an intelligent system that simplifies expense tracking by automating the extraction, categorization, and analysis of bill data — all through a seamless interface

Watch the Demo

Here’s a short demo of the Expense Manager in action:

https://youtu.be/D7h_RL118Eg

In the demo, the Expense Manager processes multiple bills to extract, categorize, and summarize expenses into structured outputs. It provides visualizations and downloadable reports, while allowing users to query the data interactively through a chat interface. This showcases the seamless integration of structured outputs, modular tools, and dynamic orchestration.

This blog takes you behind the scenes of this Expense Manager and discusses the techniques used:

1. Pydantic for handling structured data.
2. Tools for breaking tasks into modular, reusable components.
3. Agents for orchestrating the workflow and responding to dynamic user queries.

These concepts not only power the Expense Manager but also provide a reusable framework for solving other automation tasks. Curious to know more? Let’s dive into the technical magic behind the scenes!

Conceptual Background: Understanding the Core Components

To build the Expense Management Assistant, we leveraged three powerful concepts: Pydantic, Tools, and Agents. Each plays a critical role in ensuring the system is modular, robust, and efficient. Here’s a quick primer on these components:

1. Pydantic for Structured Outputs

In systems like the Expense Manager, data consistency is critical. The workflow involves multiple stages, and each stage’s output serves as the input to the next. To ensure smooth transitions and prevent errors, we rely on Pydantic for data validation and structured outputs. Pydantic is a Python library for data validation and settings management, that guarantees the types and constraints of the output model.

Why Pydantic Is Essential:

• Schema Validation: Pydantic ensures that data adheres to predefined schemas, making it predictable and consistent.
• Error Prevention: Any mismatches or anomalies in the data structure are detected early, preventing downstream errors.
• Seamless Integration: With structured outputs, every stage of the workflow communicates efficiently, ensuring robustness and reliability.

Here is an example schema used in the Expense Manager to define and validate the structured data:

With Pydantic as the backbone of structured data handling, the Expense Manager ensures a reliable and error-free workflow.

2. Tools for Modular Tasks

Tools are modular components designed to perform specific tasks. They act as external capabilities that the system can invoke to achieve well-defined objectives, such as retrieving data, running calculations, or processing user inputs. Tools make complex workflows easier to manage by breaking them into smaller, independent steps.

Why Tools Are Essential:

1. Modularity: Each tool focuses on a specific task, ensuring clear separation of responsibilities and reusability.
2. Task Specialization: Tools are purpose-built, enabling the system to call on the right resource for the right job.
3. Integration: Tools seamlessly integrate with agents, allowing the agent to dynamically decide which tool to invoke based on the task at hand.

How Tools Work:

• Tools are callable components that expose specific functionalities to the agent.
• An agent invokes a tool by passing the required inputs, and the tool returns structured outputs

Read More@ https://medium.com/perfiostechblog/building-an-expense-manager-using-genai-exploring-pydantic-tools-and-agents-03a4b67e28c7