Building an AI-Powered Ticketing Assistant
Introduction
This guide outlines the process of creating an AI-powered asynchronous ticketing assistant using PydanticAI, Pydantic v2, and SQLite. The assistant will streamline ticket management by automating ticket creation and status checking through natural language prompts.
Key Components
1. Technology Stack
- PydanticAI: A library that enables the creation of type-safe AI agents.
- Pydantic v2: Used for defining data schemas.
- SQLite: A lightweight database for storing ticket information.
- Google Gemini: An AI model that interprets natural language prompts.
2. Setting Up the Environment
To begin, ensure you have the latest version of pip and install the necessary libraries:
!pip install --upgrade pip
!pip install pydantic-ai
!pip install nest_asyncio
3. Database Configuration
We will create an in-memory SQLite database to store ticket information. The database will include a table with the following fields:
- ticket_id: Unique identifier for each ticket.
- summary: Brief description of the issue.
- severity: Urgency level (low, medium, high).
- department: Department responsible for the ticket.
- status: Current status of the ticket (initially set to “open”).
Creating the Ticketing Assistant
1. Defining Data Models
Using Pydantic, we define models to ensure that the data structure is validated:
class CreateTicketOutput(BaseModel):
ticket_id: str
summary: str
severity: Literal['low', 'medium', 'high']
department: str
status: Literal['open'] = Field('open')
2. Implementing Agents
We will create two agents:
- Create Ticket Agent: Responsible for logging new tickets.
- Status Agent: Retrieves the current status of tickets.
3. Example Use Case
Here’s how the ticketing assistant operates:
create_result = await create_agent("My printer on 3rd floor shows a paper jam error.")
status_result = await status_agent("What’s the status of ticket {ticket_id}?")
This process allows users to create tickets and check their status using simple, conversational prompts.
Case Studies and Statistics
According to a recent study by McKinsey, businesses that implement AI-driven solutions can improve operational efficiency by up to 40%. Companies like Zendesk have successfully integrated AI into their customer service operations, resulting in faster response times and improved customer satisfaction.
Conclusion
In summary, this guide demonstrates how to build an AI-powered ticketing assistant that automates ticket management through conversational AI. By leveraging PydanticAI and SQLite, businesses can ensure data integrity while enhancing customer service efficiency. As you explore AI solutions, consider starting with small projects to measure effectiveness and gradually expand your implementation.
For further assistance in managing AI in your business, feel free to reach out to us at hello@itinai.ru or connect with us on Telegram, X, and LinkedIn.
