CLTL Leolani Combot provides the framework for applications that implement human-robot interaction with conversation.
This is the successor of the Leolani platform with an improved modular architecture.
Clone one of the application parents from this project space and follow the instructions there to run them:
Currently, the following components are implemented for the framework:
-
Application Repository
Repository to share artifacts and external dependencies between components. -
EMISSOR
Representation of interaction data. -
Backend
Hardware integration and signal generation. -
Automatic Speech Recognition (ASR)
Transcription of audio signals to text. -
Voice Activity Detection (VAD)
Detection of speech in audio signals. -
Face Recognition
Currently includes face detection, age-gender detection, face recognition -
Chat UI
Simple Chat client to display and interact with the conversation -
Eliza chat
Eliza based chat. -
Get To Know You (G2KY) chat
Establish name based on face recognition. - About-agent Answer questions about the agent itself.
- Knowledge extraction Extracts factoid triples and perspectives from statements and gives back responses or it extracts SPARQL queries from questions and generates answers.
- Knowledge linking Resolves IRIs for mentions and perceptions of things and people such that triples are augmented with IRIs.
- Knowledge representation The models and functions to support the episodic Knowledge Graph.
- Language generation Generates texts from triples.
- Mention detection Detects mentions of entities and visual objects in text.
- Object recognition Detects people and objects in images.
- Question processor Answers questions about the world through the Internet.
- Visual responder Answers questions to the visual context.
To create a new component follow the instructions in the template component.
Clone one of the above applications and follow the instructions there to run them.
Homebrew is a useful package manager for Mac OS X, to install it follow the instructions on their homepage.
Most of the repositories require a Python version of at least 3.8 and at most 3.10.
For the provided build tooling the python and pip command must be linked to an
appropriate version, You can check the version and used installation with the
python --version
which python
commands. One option to manage Python versions is to use pyenv.
Note, however, that pyenv doesn't work well together with anaconda. To detect if you
are using anaconda use the command above.
On OS X you can alternatively you install specific Python version using homebrew by installing
brew install python@3.10
and adding it to your PATH variable, like
export PATH="$(brew --prefix)/opt/python@3.10/libexec/bin:$PATH"
in ~/.zshrc (see also their their documentation).
Note that using an alias for the python command in the shell configuration script does not
work as aliases are eventually not expanded if the shell is not in interactive mode.
If you are using anaconda the installation of some of the dependencies with pip can cause issues. For this reason we recommend not to use anaconda to build and run the Leolanii platform. As mentioned above, anaconda does not work well together with pyenv as both use the same mechanism to intercept the system PATH. If you are usually using anaconda to manage your Python version, one option is to set the system Python installation to a version compatible with Leolani and deactivate anaconda for the time working with Leolani. Note that anaconda typically activates the base environment by default when starting an interactive shell.
To set the system Python version with homebrew on Max OS X run
brew install python@3.10
and follow the instructions in the output messages to prepend the PATH variable in your
~/.zshrc file and add your modifications before the anaconda setup in ~/.zshrc.
To build the application, make is used.
On OS X it is recommended to upgrade make. Since OS X doesn't use standard GNU
utils due their restrictive licence, default make on OS X is way outdated.
One option is to use homebrew:
brew install make
and add the installed gmake command by adding
PATH="$(brew --prefix)/opt/make/libexec/gnubin:$PATH"
to your ~/.zshrc
Docker is a tool to run our applications or components in a
containerized runtime environment. To install it follow the instructions on their homepage
or use Homebrew. Note that you need to use the
--cask option with Homebrew!
To check if Java is installed on your system you can run
java --version
in the command line. If this does not work, install Java, e.g. with
brew install openjdk
Some components use GraphDB, to install it register on their homepage and follow the provided instructions.
Some dependencies require a C compiler to be installed. On Mac OS X you may need to install
sudo xcode-select —install
If you encounter error messages regarding an invalid version of clang, you may need to reinstall by first running
sudo rm -rf /Library/Developer/CommandLineTools
followed by the installation command above.
Some dependencies require a Rust compiler to be installed, follow the instructions on their homepage to install it.
Python audio libraries may need portaudio to be installed, on Mac OS X you can use homebrew to install it. To figure out specific instructions regarding your hardware a simple internet search should find you the answers.
Also libsndfile and ffmpeg may need to be installed on your system.
Mac OS X the above can be installed with homebrew:
brew install portaudio libsndfile ffmpeg
It is possible though that homebrew does not link the libsndfile properly, in this case follow
the instructions in this stackoverflow post
and pay attention to the output of homebrew. A likely fix is to add the following line to your
shell initialization script (~/.zshrc):
export DYLD_LIBRARY_PATH="/opt/homebrew/lib:$DYLD_LIBRARY_PATH"
Pillow eventually needs additional system libraries to be installed, check the External Libraries section in their installation instructions if you run into errors related to Pillow.
To work on the development of a specific application, start from the parent repository and follow the steps described below. The description uses the Eliza app as example.
To check out all code needed for the Eliza App, follow the instructions in the Eliza app.
The application is structured into separate components which have their own git repositories and can be run as separate Python applications. The parent repository of the application contains all those component repositories as git submodules.
There is a central application (cltl-eliza-app) that configures and runs all the necessary components it needs, either inside a Python application or as containerized services in a Kubernetes cluster or using docker compose. To run the application, first all components need to be packaged and made available to the application. For this purpose there are makefiles available in the components and the application parent that automate this process. To build the application run
make build
from the parent repository. This command will download external dependencies to cltl-requirements, setup virtual environments for all components, package them and publish the packages to cltl-requirements to make them available to the application and other components.
To run the application follow the instructions in the Eliza qparent.
Individual components in the parent repository are edited and committed separately, and, after a stable version is
reached, the state of the components is commited in the parent repository, for the workflow see
Working with git submodules.
Modularization allows developing components in isolation. The application and other components depend on a packaged
version of a component only, therefore changes will become available outside of the component only after rebuilding
the application, see above.
To use PyCharm for development see the instructions in Workflow using PyCharm.
To commit changes made to the application see the instructions in Working with git submodules.
To add a new component to an application follow the instruction in the template component.
- Workflow using PyCharm
- Working with git submodules
- Setup a new component
- Add a component to a Python app
This repo provides infrastructre and general code for the platform:
The cltl.combot.infra module contains library code for infrastructre used in
qthe application.
Components of the application can communicate via an event bus. The
cltl.combot.infra.event module provides the interface and different implementations
of the event bus.
The cltl.combot.infra.topic_worker module provides a convenience class to
implement the subscription to one or multiple topics in the event bus.
Configuration is made available in the application via a configuration manager.
The cltl.combot.infra.config module provides the interface and different
implementations of the configuration manager.
Access to resources in the application is made available via a resource manager.
This includes providing resources and waiting for resources to become available
as well as managing access to shared resources. The cltl.combot.infra.resource
module provides the interface and different implementations of the resource
manager.
The cltl.combot.infra.time_util module provides time related utilities to
ease the usage of a consistent time format throughout the application.
The cltl.combot.infra.di_container module provides a simple utility to use
dependency inject in the application.
To be added.
The cltl.combot.event module contains common event payloads.
Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the MIT License. See LICENSE for more
information.
