vaishnavi2204b/metaaihackhathon

OpenEnv: Customer Support Agent Environment

Overview

This environment simulates a real-world customer support ticket management system. It is designed to train and evaluate RL agents on tasks like ticket triage (categorization), data extraction, and polite response generation.

Task Simulation

The environment represents tasks that humans perform in customer support roles:

• Categorization: Identifying the type and priority of a ticket.
• Extraction: Pulling specific data points (like Order IDs) from unstructured text.
• Response: Drafting helpful and polite replies based on knowledge base snippets.

OpenEnv Specification

This implementation fully complies with the OpenEnv interface:

• Typed Models: Uses Pydantic for Observation, Action, and Reward models.
• Standard API: Implements step(action), reset(), and state().
• Metadata: Defined in openenv.yaml.

Tasks and Graders

The environment includes three tasks of increasing difficulty:

1. Basic Triage (Easy): Categorize a billing inquiry.
2. Data Extraction (Medium): Extract Order ID and Email from a technical request.
3. Complete Resolution (Hard): Generate a polite response for account recovery.

Each task has a programmatic grader that assigns a score between 0.0 and 1.0 based on accuracy and quality.

Action & Observation Spaces

Observation Space

• current_ticket: Object containing ticket content and metadata.
• history: List of previously completed tasks in the session.
• knowledge_base_snippet: Relevant text for the current task.

Action Space

• action_type: One of categorize, extract_info, respond.
• category: Ticket category (billing, technical, etc.).
• priority: Ticket priority (low, medium, high, urgent).
• extracted_data: Dictionary of extracted fields.
• response_text: The generated response string.

Setup and Usage

Local Development (Python)

1. Install dependencies: pip install -r requirements.txt
2. Run baseline inference: python inference.py

Deployment

This environment is ready for deployment on Hugging Face Spaces using the provided Dockerfile.

Baseline Performance

The provided inference.py script serves as a baseline using GPT-4o. Expected scores:

• Easy: 1.0
• Medium: 1.0
• Hard: 0.8+