Introduction

In today’s fast-paced development environments, managing and categorizing tickets efficiently is crucial for maintaining productivity and ensuring critical issues are addressed promptly. However, manual classification of tickets can be time-consuming and prone to human error. This is where GenKit Parser comes into play - an open-source solution that leverages the power of Google’s Gemini AI to automate and streamline the ticket classification process.

The Problem

Traditional ticket management systems often require manual input for:

  • Severity assessment
  • Component identification
  • Priority assignment

This manual process not only consumes valuable developer time but also introduces inconsistencies in classification. Different team members might categorize the same issue differently, leading to confusion and potential delays in issue resolution.

The Solution: GenKit Parser

GenKit Parser is a Go-based library that uses Google’s Gemini AI to automatically analyze ticket descriptions and extract key information. While we provide a basic implementation for ticket classification, the framework is highly flexible and can be adapted for various use cases. Here’s how the base implementation works:

  1. Natural Language Processing: The system takes any text input in natural language
  2. AI-Powered Analysis: Google’s Gemini AI processes the text based on your custom requirements
  3. Structured Output: The system returns data structured according to your needs

Customization Possibilities

The example implementation shows ticket classification, but you can adapt GenKit Parser for various scenarios:

  1. Custom Data Structures

    • Define your own structs based on your needs
    • Add additional fields and classifications
    • Create complex nested structures

  2. Different Classification Types

    • Customer feedback categorization
    • Support request prioritization
    • Content moderation
    • Document classification
    • Sentiment analysis

  3. Custom Processing Rules

    • Define your own severity levels
    • Create custom component categories
    • Implement domain-specific classification rules

Example Implementation

Here’s a basic example of ticket classification, but remember this is just one possible use case:

// Example ticket structure - you can define your own!
type Ticket struct {
    Severity  Severity `json:"severity"`
    Component string   `json:"component"`
    // Add your own fields here
    Priority    int      `json:"priority"`
    Tags        []string `json:"tags"`
    Department  string   `json:"department"`
    Complexity  string   `json:"complexity"`
}

// Example custom severity levels
type Severity string
const (
    LOW    = "low"
    NORMAL = "normal"
    HIGH   = "high"
    // Define your own severity levels
    CRITICAL = "critical"
    URGENT   = "urgent"
)

// Basic usage example
func main() {
    var result Ticket
    err := classify.Exact(classify.ExactOptions{
        Model:  classify.GoogleGemini,
        APIKey: os.Getenv("GEMINI_API_KEY"),
        Input:  "Your input text",
        Output: &result,
        // Add your custom options here
        Rules: YourCustomRules{},
        Config: YourCustomConfig{},
    })
}

Alternative Use Cases

  1. Content Moderation
type Content struct {
    Category    string   `json:"category"`
    SafetyLevel string   `json:"safety_level"`
    Keywords    []string `json:"keywords"`
}
  1. Customer Feedback Analysis
type Feedback struct {
    Sentiment    string   `json:"sentiment"`
    Topics       []string `json:"topics"`
    Urgency      int      `json:"urgency"`
    ActionNeeded bool     `json:"action_needed"`
}
  1. Document Classification
type Document struct {
    DocumentType string   `json:"document_type"`
    Departments []string `json:"departments"`
    Confidence  float64  `json:"confidence"`
}

Getting Started

The project is open-source and designed to be easily customizable. Start with our basic implementation and modify it to suit your needs:

  1. Fork the Repository
  2. Define Your Structures
  3. Customize Classification Logic
  4. Add Your Business Rules

Conclusion

GenKit Parser provides a flexible framework that you can adapt to your specific needs. While we demonstrate ticket classification as an example, the possibilities are endless. Whether you’re building a content moderation system, analyzing customer feedback, or creating your own unique classification system, GenKit Parser can be customized to meet your requirements.

The project is actively maintained and open to contributions. We encourage you to experiment, customize, and share your implementations with the community.

Join the conversation on GitHub and share your unique implementations!