hello-ltr

The overall goal of this project is to demonstrate all of the steps required to work with LTR in Elasticsearch or Solr. Follow the setup instructions and check out the LTR notebooks in Solr or Elasticsearch

Setup your search engine

LTR examples here for Solr or Elasticsearch which require the right search engine to be installed

Setup Solr w/ LTR

With Docker installed, a script will launch Solr & the config under the solr/ dir in the console:

cd docker/solr
./launch_solr.sh

Or manually

Go into the "Solr" docker directory: cd docker/solr
Run docker build . -t ltr-solr to create a image running Solr with LTR
Start the instance by running: docker run --name ltr-solr -p 8983:8983 -d ltr-solr
Subsequently run with docker start ltr-solr and docker stop ltr-solr

Setup Elasticsearch w/ LTR

With Docker installed, a script will launch Elasticsearch w/ Kibana tooling in the console:

cd docker/elasticsearch
./launch_es.sh

Manually build & run the containers

# Create Elasticsearch
cd es-docker
docker build -t ltr-elasticsearch .

# Create Kibana
cd kb-docker
docker build -t ltr-kibana .

# Launch
cd ..
docker-compose up

Setup & Run Jupyter Notebook Examples

Setup Python requirements

Ensure Python 3 is installed on your system
Create a virtual environment: python3 -m venv venv
Start the virtual environment: source venv/bin/activate
Install the requirements pip install -r requirements.txt

Note: The above commands should be run from the root folder of the project.

Start Jupyter notebook and confirm operation

Run jupyter notebook
Open either the "hello-ltr (Solr)" or "hello-ltr (ES)" as approriatte and ensure you get a graph at the last cell

Getting Started

Run jupyter notebook and load the hello-ltr notebook
Run thru each cell to get more familiar with the LTR pipeline

Docker Compose

If you hit any snags with the JDK or python dependencies, the docker folder has a docker-compose configuration that prepares an environment to run all of the notebooks.

jeancsil/hello-ltr