This framework aims to offer an easy way to develop and deploy chatbots, reducing the overhead in code by using standardized components, allowing a high degree of exchangeability. The easy setup also enables less experienced developers to build and deploy a chatbot. Different messaging platforms (like Facebook Messenger, Slack or your personal website) can be used interchangeably to communicate with a user, and requests can be classified and answered using different natural language processing (NLP) services like Dialogflow, Snips or Rasa, depending on your personal need. Changing a platform does not influence the logic in the background, meaning that you only have to change very few lines of code to change from one platform to another while maintaining the same functionality. Further, the separation of concerns makes it simple to include further functionality like automated speech recognition.
Separating the different components is especially useful to preserve and protect the privacy of both, the user and the host of a chatbot. You can also combine multiple agents (chatbots) in a single application to separate the functionality of a bot into multiple logically separated units which are combined to one coherent bot.
The mid-term goal is to further reduce the required programming knowledge to setup a bot, and improving the protection of the users' privacy by incorporating additional means of protection, like the pseudonymization of users, per default. These means can also be included at the current state, but are not enabled by default.
For a minimal setup, without these protecting means, please consult this repository. The minimal setup should only be used to get to test the basic functionality of the framework. We highly recommend to adapt our more extensive example, which comes with a slightly more complex setup but enables you to e.g. use a database and pseudonymize users identifiers.
Offering this framework hopefully enables many individuals and (also small) companies, reducing the time and budget that has to be spent to develop a chatbot, while maintaining a high degree of control.
The easiest way to test the framework is by using your console instead of a chat application like Facebook or Slack, since these platforms require you to setup a server with a certificate to communicate over https. To just test the basic setup, you can follow these steps:
- Install the dependencies (npm required): open a terminal and type
npm install. - Compile the code (
npm run tsc) and run your bot (npm run start). The framework will now await messages. - Open another terminal and start the CLI client:
npm run start-cli. Just type a message and hit enter to check if everything works out.
You will receive a message telling you that you did not set up a NLP service yet. If you want to connect your CLI to an existing NLP service:
- Create an agent using a supported NLP service of your choice, get the required credentials and paste them into the configuration file (more information on these can be found at the distinctive section in our docs (see below).
- Choose the configuration in your
src/main.ts. Make sure to use the correct configuration file with the correct adapters (the CLI adapter and the adapter for the NLP service you just chose to use).
If you want to deploy your bot to a messaging service such as Slack, you have to get the respective credentials (see docs) required to authenticate. Furthermore, you usually have to setup a server and communicate over https. A detailed setup will follow soon.
| Feature | Slack | CLI Client | |
|---|---|---|---|
| Authentication | done | done | done |
| Verify payload | done | done | not done, not planned |
| Text requests | done | done | done |
| Image requests | done | not done | not done, not planned |
| initial requests | done | done | done |
| isFromAdmin | done | not done | not done, not planned |
| undefined | done | done | done |
| Text responses | done | done | done |
| URL button responses | done | not done | not done, not planned |
| Image responses | done | not done | not done, not planned |
| Image with title responses | done | not done | not done, not planned |
| Quickreply | done | done | not done |
| Undefined | done | done | not done, not planned |
| Feature | DialogflowV2 | Snips | Rasa |
|---|---|---|---|
| sendTextRequest | done | done | done |
| deleteSelectedContexts | done | done* | done* |
| deleteAllContexts | done | done* | done* |
| postContexts | done | done* | done* |
* Snips and Rasa have no context system in the same fashion as Dialogflow. As such the methods are implemented to be used when an additional context system is added to those systems.
Please consult the docs for further information regarding the setup, supported platforms, configuration files or details regarding the architecture.
Our API reference is built using compodoc, while we use sphinx for the description of the framework itself. You can follow the next steps if you would like to compile and change parts of the documentation (commands are valid for Ubuntu and might change across operating systems):
- Install pip (e.g.
apt-get install python3-pip). - Optional, but recommended: install virtualenv
pip install virtualenv, create a virtual environment (virtualenv sphinxenv) and start the environment usingsource sphinxenv/bin/activate. pip install -r requirements.txt(add the--userflag if you are not in a virtualenv).- Make sure to install npm and run
npm run createDocs.
A primer for restructured text (which is the markup language used by Sphinx) can be found here.
Logging is controlled in ./src/logger.ts using winston. Logs are written to stdout as well as two files: One file for errors only and one file with additional information, determined by your loglevel, which also determines the logs for stdout.
The default log level is set to "verbose" (more information can be found here). Please set the respective environment variable if you wish to change the loglevel (e.g.export LOGLEVEL="error").
You would like to contribute? Awesome! Please check out our Contribution Guidelines.
This project is licensed under the Apache 2.0 License - see the LICENSE.md file for details.
- Fiete Lüer
- Maxim Dolgich
- Bastian Gorholt
- Raphael Arias
- Tabea Spahn