chatbot-connectors 0.8.0

A unified Python library for connecting to various chatbot APIs (RASA, MillionBot, Taskyto)

Chatbot Connectors

A Python library for connecting to various chatbot APIs with a unified interface.

Installation

pip install chatbot-connectors

Custom YAML Connector

If there is no connector for your chatbot and you are not willing to code one, you can use the Custom Connector. What this one does is read a YAML file with the info and try to work that way.

To see how to build these YAML files and use them see CUSTOM CONNECTOR GUIDE, there are also examples in the yaml-examples directory.

If you want to directly try one, execute this in a Python shell:

from chatbot_connectors.implementations.custom import CustomChatbot

bot = CustomChatbot("yaml-examples/ada-uam.yml")
success, response = bot.execute_with_input("Hola, necesito ayuda con Moodle")
print(response)

Built-in Connectors

The library ships with several ready-to-use connectors. Each connector exposes the parameters listed via --list-connector-params in the CLI or get_chatbot_parameters() in code.

Botslovers

• Only required parameter: base_url.
• Base URL examples: https://arthur.botslovers.com/, https://alcampo.botslovers.com/
• Minimal Python usage:

  from chatbot_connectors.implementations.botslovers import BotsloversChatbot
  
  bot = BotsloversChatbot(base_url="https://arthur.botslovers.com/")
  success, reply = bot.execute_with_input("Hi Arthur!")
  print(reply)
  

Metro de Madrid

• Uses Metro Madrid's public website widget API and auto-creates a session.
• Requires a handshake that sends the first message, selects the language, and accepts the privacy policy; the connector performs this sequence automatically using the language parameter ("es" by default, accepts "en").
• Example:

  from chatbot_connectors.implementations.metro_madrid import MetroMadridChatbot
  
  bot = MetroMadridChatbot(language="es")
  success, reply = bot.execute_with_input("¿A qué hora cierra hoy el metro?")
  print(reply)
  

Comunidad de Madrid

• Uses Comunidad de Madrid's public avatar widget API.
• Auto-generates a conversation_id; pass one captured from the browser if the widget requires an existing session.
• Example:

  from chatbot_connectors.implementations.comunidad_madrid import ComunidadMadridChatbot
  
  bot = ComunidadMadridChatbot()
  success, reply = bot.execute_with_input("Hola")
  print(reply)
  

MillionBot

• Requires a bot_id. Known deployments:
  • ADA UAM: 60a3be81f9a6b98f7659a6f9
  • SAIC Malaga: 64e5d1af081211d24e2cfec8
  • Madrid te cuida: 612cc0d871562c07747d3f0a
  • Genion: 65157185ba7cc62753c7d3e2
  • Gallo de Morón de la Frontera: 65ca19e7dbbb4e26cbeadf24
  • Ayto. de Arucas: 660d8b37876b1f546abde807
  • Gestri Diputación Valencia: 6141bc1e161c3d4e06ced69c
• Quick example:

  from chatbot_connectors.implementations.millionbot import MillionBot
  
  bot = MillionBot(bot_id="60a3be81f9a6b98f7659a6f9")
  success, reply = bot.execute_with_input("Hola, ¿puedes ayudarme?")
  print(reply)
  

RASA

• Use the public REST webhook, e.g. base_url="http://localhost:5005".
• Optional sender_id controls conversation tracking.

Taskyto

• Requires the Taskyto server base URL and optional port (defaults to 5000).
• Example: ChatbotTaskyto(base_url="http://localhost", port=8080)