Tutorial on the implementation of the email automatic reply assistant (Rasa/SMTP)

In modern office scenarios, handling large amounts of emails is a time-consuming and error-prone task. In order to improve work efficiency, we can use natural language processing (NLP) and Mail Transfer Protocol (SMTP) technologies to build an intelligent email automatic reply assistant. This article will introduce in detail how to use Python's Rasa framework and SMTPlib library to implement this function, helping readers master the integration methods of NLP model training and business system, and understand dialogue system design.

1. Introduction

1.1 The concept of automatic email reply assistant

The Email Auto-Reply Assistant is a tool that automatically analyzes email content and generates replies based on preset rules or machine learning models. It can help users quickly process large amounts of emails, improve work efficiency, and reduce human errors.

1.2 Advantages of using Rasa and SMTP

• Rasa Framework:Rasa is an open source machine learning framework designed specifically for building conversational systems. It provides powerful natural language understanding (NLU) and dialogue management (Core) capabilities, which can train accurate intent recognition models and dialogue strategies.
• SMTP protocol:SMTP (Simple Mail Transfer Protocol) is a standard protocol for sending and receiving emails. Python's smtplib library provides support for the SMTP protocol, making it simple and efficient to automatically send and receive emails.

2. Technical Overview

2.1 Introduction to Rasa Framework

Rasa consists of two core modules:

• Rasa NLU: Responsible for natural language understanding, converting the text entered by the user into structured intentions and entities.
• Rasa Core: Responsible for dialogue management, and decide the next reply action based on the current dialogue history and preset dialogue strategy.

2.2 SMTP protocol and smtplib library

The SMTP protocol defines communication rules between a mail client and a mail server. Python's smtplib library provides an interface to implement the SMTP protocol, allowing us to send and receive emails by writing Python code.

2.3 Introduction to Tkinter Library

Tkinter is a standard GUI library for Python that can be used to create desktop applications. In the email auto-reply assistant, we can use Tkinter to develop a desktop notification system that displays new emails and replies suggestions in real time.

3. Detailed tutorial

3.1 Building an email classification intent recognition model

3.1.1 Prepare the dataset

We use the dataset provided by the /gh_mirrors/em/EmailIntentDataSet project, which contains sentence-level verbal behavior annotations in multiple email scenarios.

3.1.2 Training Rasa NLU model

1. Install Rasa：

   bash copy code
   
    pip install rasa

2. Create a Rasa project：

   bash copy code
   
    rasa init

3. Define intentions and entities：
   existdata/Define email intent in the file, for example:

   nlu:
   - intent: request_information
     examples: |
       - Can you provide more details about the project?
       - I need some information about the meeting.
    - intent: confirm_appointment
     examples: |
       - The meeting is confirmed for tomorrow.
       - Yes, I can attend the meeting.
   

4. Training NLU models：

   bash copy code
   
    rasa train nlu

3.1.3 Testing NLU model

Test model performance using the interactive interface provided by Rasa:

bash copy code

 rasa interactivity

3.2 Training dialogue management strategy

3.2.1 Defining a conversation story

existdata/Define a conversation story in the file and describe the interaction process between the user and the assistant:

stories:
- story: request_information_story
  steps:
  - intent: request_information
  - action: utter_provide_information
- story: confirm_appointment_story
  steps:
  - intent: confirm_appointment
  - action: utter_appointment_confirmed

3.2.2 Configuration domain and response

existDefine the realm and response in the file:

intents:
- request_information
- confirm_appointment
 
responses:
  utter_provide_information:
  - text: "Sure, here are the details you requested."
  utter_appointment_confirmed:
  - text: "Great, the appointment is confirmed."

3.2.3 Training dialogue management model

bash copy code

 rasa train core

3.3 Integrated Mail Client API

3.3.1 Send emails using smtplib

import smtplib
from  import MIMEText
 
def send_email(subject, body, to_email):
    msg = MIMEText(body)
    msg['Subject'] = subject
    msg['From'] = 'your_email@'
    msg['To'] = to_email
 
    with smtplib.SMTP_SSL('', 465) as server:
        ('your_email@', 'your_password')
        server.send_message(msg)

3.3.2 Use imaplib to receive mail

import imaplib
import email
 
def check_emails():
    mail = imaplib.IMAP4_SSL('')
    ('your_email@', 'your_password')
    ('inbox')
 
    _, data = (None, 'UNSEEN')
    email_ids = data[0].split()
 
    for e_id in email_ids:
        _, msg_data = (e_id, '(RFC822)')
        msg = email.message_from_bytes(msg_data[0][1])
        print(f'Subject: {msg["Subject"]}')
        print(f'From: {msg["From"]}')
        print(f'Body: {msg.get_payload()}')
 
    ()

3.4 Develop a desktop notification system

3.4.1 Create notification interface using Tkinter

import tkinter as tk
from tkinter import messagebox
 
def show_notification(title, message):
    root = ()
    ()
    (title, message)
    ()

3.4.2 Integrated email checking and notification functions

def monitor_emails():
     While True:
         check_emails()
         # If there is a new email, call show_notification to display notifications
         (60000, monitor_emails) # Check emails every 60 seconds
 
 root = ()
 (0, monitor_emails)
 ()

4. Results display

Through the above steps, we have built a complete email auto-reply assistant that is able to:

• Automatically check new emails and extract content.
• Use the Rasa NLU model to identify email intent.
• Select a preset reply template based on your intention or generate a reply suggestion.
• Send reply emails via smtplib.
• Use Tkinter to provide desktop notifications.

V. Conclusion

This article details how to use Rasa and SMTPlib to implement the email auto-reply assistant, including building an intent recognition model, training conversation management policies, integrating the email client API, and developing a desktop notification system. Through this tutorial, readers can master the integration methods of NLP model training and business system, understand dialogue system design, and be able to apply the knowledge learned to actual office scenarios to improve work efficiency.

---

Code Sample Integration

Here is the complete code that integrates the above code examples:

# Complete code of the email auto-reply assistant
 
 import smtplib
 import imaplib
 import email
 import tkinter as tk
 from tkinter import messagebox
 from import Interpreter
 
 # Initialize the Rasa NLU interpreter
 interpreter = ('models/nlu/default/model_20230414-123456')
 
 def send_email(subject, body, to_email):
     msg = MIMEText(body)
     msg['Subject'] = subject
     msg['From'] = 'your_email@'
     msg['To'] = to_email
 
     with smtplib.SMTP_SSL('', 465) as server:
         ('your_email@', 'your_password')
         server.send_message(msg)
 
 def check_emails():
     mail = imaplib.IMAP4_SSL('')
     ('your_email@', 'your_password')
     ('inbox')
 
     _, data = (None, 'UNSEEN')
     email_ids = data[0].split()
 
     for e_id in email_ids:
         _, msg_data = (e_id, '(RFC822)')
         msg = email.message_from_bytes(msg_data[0][1])
         email_subject = msg["Subject"]
         email_body = msg.get_payload()
         email_from = msg["From"]
 
         # Use Rasa NLU to parse email content
         result = (email_body)
         intent = result['intent']['name']
 
         # Generate a reply based on intent
         if intent == 'request_information':
             reply = "Sure, here are the details you requested."
         elif intent == 'confirm_appointment':
             reply = "Great, the appointment is confirmed."
         else:
             reply = "Thank you for your email. We will get back to you shortly."
 
         # Send a reply email
         send_email(f'Re: {email_subject}', reply, email_from)
 
         # Show desktop notifications
         show_notification('New Email', f'From: {email_from}\nSubject: {email_subject}')
 
     ()
 
 def show_notification(title, message):
     root = ()
     ()
     (title, message)
     ()
 
 def monitor_emails():
     While True:
         check_emails()
         (60000, monitor_emails) # Check emails every 60 seconds
 
 if __name__ == '__main__':
     root = ()
     (0, monitor_emails)
     ()

Instructions for use

1. Install the dependency library：

   bash copy code
   
    pip install rasa smtplib imaplib email tkinter

2. Training Rasa Model：

   • Follow the steps in Sections 3.1 and 3.2 to train the NLU and Core models.

3. Configure mail server information：

   • Replace in codeyour_email@andyour_passwordFor the actual email address and password.
   • Replace according to the configuration of the email service providerandFor the correct SMTP and IMAP server addresses.

4. Run the code：

   bash copy code
   
    python email_autoreply_assistant.py

Through the above steps, you can have a complete email automatic reply assistant.