A chatbot that answers OpenStreetMap related queries. It is powered by Rasa Open Source.

Have a look at our blog post: https://blog.kathmandulivinglabs.org/introducing-a-new-member-to-the-openstreetmap-community/

Have a conversation with chatbot at: https://www.facebook.com/osmchatbot

RASA

Missing Maps

Tag Finder

Contributions are always welcome!

To contribute to this project you can add/update Natural Language Understanding Data and bot responses.

Clone this project

git clone https://github.com/KathmanduLivingLabs/OSM-chatbot.git

Go to the project directory

cd OSM-chatbot

Rasa Open Source uses YAML as a unified and extendable way to manage all training data, including NLU data, stories and rules. You can split the training data over any number of YAML files, and each file can contain any combination of NLU(Natural Language Understanding) data, stories, and rules. The training data parser determines the training data type using top level keys.

To add training data you must have to add intent on one of the nlu files inside data folder. For example if i want to add chatbot support for iD editor info:

• Open faq.yml located inside data folder
• Add intent as faq/iDeditor_info where faq is our retrieval intent. What is retreival intent?
• Add at least 5 examples for this intent. This is where our model learns to predict user intent (e.g. What is iDeditor?)
• Add response for this intent on responses.yml present inside /data/resposnes and on domain.yml file.

Clone the project

  git clone https://github.com/KathmanduLivingLabs/OSM-chatbot.git

Go to the project directory

  cd OSM-chatbot

Create virtual environment

    python3 -m venv ./venv

Activate environment

    source venv/bin/activate

Install dependencies

    pip install -r requirements.txt

To train model

    rasa train

To chat with bot on command line

    rasa shell

To start rasa server

    rasa run

or to run rasa with api enabled

    rasa run --enable-api

Custom action server are required to fetch dynamic response from api's or web scrapping. We have used custom actions to fetch user statistics and tag information in this project.

To run custom action server

  cd actions
  pip install -r requirements.txt
  rasa run actions

To run on interactive mode on web browser:

• Make sure you have RasaX installed on your environment, Then run

rasa x

• Currently the chatbot only supports English language, we want to extend its support for as many languages as possible.
• This chatbot model is trained on a very limited set of training data. We want to add support for more user queries.
• We want to add support for more OSM tools like running an Overpass query in natural language through chatbot, generating before-after maps, connecting users to local OSM-communities by accessing their location and others.