# Email Extractor
The Email Extractor is a specialized tool designed to identify and extract email addresses from any given text input. This agent scans the input provided by the user, locating any email addresses, and presents each one on a separate line in its original format. If no email addresses can be found, the agent will simply return 'N/A'. This utility is especially useful for data collection, analysis, and organization tasks where email addresses are embedded within larger texts.
# Getting Started
To get started, make sure you have cloned the Email Extractor. Follow Create from Template to clone and configure your AI agent.
# Configuration Instructions
# Knowledge
You can upload .pdf or .txt files to provide the agent with tailored context that meets your specific requirements.
# User Inputs
| Field Title | Description | Instruction |
|---|---|---|
| Text for Process | The text from which email addresses will be extracted | Provide a clear and concise block of text. The length can vary, but ensure it is legible and contains potential email addresses. Focus on complete sentences where emails may naturally appear. |
# AI Models
The default model is Llama 3.1 8B Instruct, but you can explore other supported AI models as well. If needed, refine your prompts for more tailored outputs.
TIP
For more detailed instructions on configuring an AI agent, please refer to Configure AI Agent
# Sample Inputs and Output
# Sample Inputs
- Text for Process: Please contact us at support@example.com or sales@domain.com for more information.
# Sample Output
support@example.com
sales@domain.com
# Tips of Effective Configuration
- Be clear and concise: Ensure the text you provide is straightforward to improve the chances of accurate extraction.
- Use varied examples: If possible, include different types of texts to help the agent adapt and improve its extractions over time.
- Check for common formatting issues: Avoid typos or unusual symbols around email addresses which may hinder detection.
- Start with a smaller text block: It’s often helpful to test the function with a shorter text initially before scaling up your inputs.
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