This package contains the NLP component in the backend of the system architecture of the Fruit Fly Brain Observatory. The NLP component processes natural language queries into structured queries that can be used on NeuroArch Database.
NOTE This component requires an instance of the FFBO processor component to be running and knowledge of the processor IP address (if FFBO processor is running on a different machine). This is accomplished by setting up and attaching this image to the 'ffbonet' network, which is described below in the Installation and Execution section.
Options for installing and running ffbo.nlp_component are explained below.
NOTE If you are using a Docker image to run ffbo.nlp_component, you will need the 'ffbonet' network initialized. You can check to see if it exists via
docker network ls
If it does not, it can be initialized via
docker network create -d bridge ffbonet
Please note that the 'bridge' driver provides a network that is limited to the host machine's Docker daemon, so images within it cannot communicate with external docker hosts or Docker daemons. If you would like to communicate between computers, please use the 'overlay' driver
docker network create -d overlay ffbonet
Installing via the Docker Hub repository is recommended for non-developers. The image is installed directly onto your local Docker daemon, from which you can run it in a container. Installation is as follows:
docker pull fruitflybrain/ffbo.nlp_component
Once the image is installed, you can run it in a container:
docker run -t --net ffbonet --name ffbo.nlp_component fruitflybrain/ffbo.nlp_component
Installing via the Github repository is recommended for developers.The code is downloaded as follows:
git clone https://github.com/fruitflybrain/ffbo.nlp_component
Building and running the repository is simplified with Docker Compose, which stores the configuration for a service (such as network and name for the container and the Dockerfile to build from) in a docker-compose.yml file, simplifying the command-line call. Building and running the Docker image can be accomplished with:
docker-compose build
docker-compose run
Note that the container can be both built and run with the following command:
docker-compose up
Downloading and building the repository and image are accomplished the same as in the above section. Accessing the bash interface for the container can be accomplished with:
docker run -P -it --net ffbonet --name ffbo.nlp_component fruitflybrain/nlp_component:local bash
Running the server is done with:
sh run_component_docker.sh ws://ffbo.processor:8081/ws --no-ssl
or
export PYTHONPATH=/neuroarch_nlp:/quepy:/usr/local/lib/python2.7/site-packages:/usr/lib/python2.7/dist-packages/:$PYTHONPATH
python nlp_component.py --url ws://ffbo.processor:8081/ws --no-ssl
This will launch NLP component and start the connection between it and the Processor component.
FFBO components are configured using .ini files. If you are building and running Docker images on your local computer from the Github repository (including Docker Compose), you can configure the NLP Component via the './config.ini' file in the main directory of this repository. However, if you are downloading images directly from Docker Hub, you will need to create a '.ffbolab' folder in your computer's home directory. Into this directory, place a .ini config file referring to this component. This can be done in one of two ways. Either copy the default config file from the main directory of this repository via:
cp config.ini ~/.ffbo/config/ffbo.nlp_component.ini
or, in the case that you don't have this repository installed, via:
wget -o ~/.ffbo/config/ffbo.nlp_component.ini https://cdn.rawgit.com/fruitflybrain/ffbo.nlp_component/master/config.ini
Once you have configured the .ini file, you can run it with:
docker run -P -it --net ffbonet --name ffbo.nlp_component -v ~/.ffbo/config:/config fruitflybrain/ffbo.nlp_component:local
Or equivalently for other build methods. If you have configured a port, make sure to expose it by adding the '-p [INTERNAL PORT]:[EXTERNAL PORT]', where the internal port is the port you configured in the .ini file and the external port is the port on localhost that the output of the internal port is mapped to. Running without docker is the same process described above in the Manual Execution section.