Email Data Extraction with OpenAI Agents SDK

This example demonstrates how to use the OpenAI Agents SDK with RagaAI Catalyst to extract structured information from emails.

Overview

The application uses OpenAI's Agents SDK to parse unstructured email text and extract key information such as:

• Email subject and sender details
• Main discussion points
• Meeting information (date, time, location)
• Action items and tasks with assignees
• Next steps

The extracted data is structured using Pydantic models for easy manipulation and validation.

Requirements

• Python 3.8+
• OpenAI API key
• RagaAI Catalyst credentials

Installation

1. Clone the repository
2. Install the required dependencies:

pip install -r requirements.txt

3. Copy sample.env to .env and fill in your API keys:

cp sample.env .env

Environment Variables

Configure the following environment variables in your .env file:

• OPENAI_API_KEY: Your OpenAI API key
• CATALYST_ACCESS_KEY: Your RagaAI Catalyst access key
• CATALYST_SECRET_KEY: Your RagaAI Catalyst secret key
• CATALYST_BASE_URL: RagaAI Catalyst base URL
• PROJECT_NAME: Name for your project in RagaAI Catalyst (default: 'email-extraction')
• DATASET_NAME: Name for your dataset in RagaAI Catalyst (default: 'email-data')

Usage

Run the example script:

python data_extraction_email.py

The script will:

1. Initialize the RagaAI Catalyst client for tracing
2. Set up an OpenAI Agent with appropriate instructions
3. Process a sample email to extract structured data
4. Display the extracted information

Customization

You can modify the sample_email variable in the script to process different emails, or adapt the code to read emails from files or an API.

The Pydantic models (Person, Meeting, Task, EmailData) can be extended to capture additional information as needed.

Integration with RagaAI Catalyst

This example integrates with RagaAI Catalyst for tracing and monitoring agent interactions. The integration helps with:

• Tracking agent performance
• Debugging complex agent workflows
• Collecting data for future improvements