Hello LTR :)

The overall goal of this project is to demonstrate all the steps required to work with LTR in Elasticsearch, Solr, or OpenSearch. There are two modes of running this project. You can run and edit notebooks in a docker container or you can do local development on the notebooks and connect to the search engine(s) running in Docker.

No fuss setup: You just want to play with LTR

Follow these steps if you're just playing around & are OK with possibly losing some work (all notebooks exist just in the docker container)

With docker simply run

docker compose up

at the root dir and go to town!

This will run jupyter and all search engines in Docker containers. Check that each is up at the default ports:

• Solr: localhost:8983
• Elasticsearch: localhost:9200
• Kibana: localhost:5601
• OpenSearch: localhost:9201
• OpenSearch Dashboards: localhost:5602
• Jupyter: localhost:8888

You want to build your own LTR notebooks

git Follow these steps if you want to do more serious work with the notebooks. For example, if you want to build a demo with your work's data or something you want to preserve later.

Run your search engine with Docker

You probably just want to work with one search engine. So whichever one you're working with, launch that search engine in Docker.

Running Solr w/ LTR

Setup Solr with docker compose to work with just Solr examples:

cd notebooks/solr
docker compose up

Running Elasticsearch w/ LTR

Setup Elasticsearch with docker compose to work with just Elasticsearch examples:

cd notebooks/elasticsearch
docker compose up

Running OpenSearch w/ LTR

Setup OpenSearch with docker compose to work with just OpenSearch examples:

cd notebooks/opensearch
docker compose up

Run Jupyter locally w/ Python 3 and all prereqs

Setup Python requirements

• Ensure Python 3.8 or later is installed on your system
• Create a virtual environment: python3 -m venv venv
• Start the virtual environment: source venv/bin/activate
• Check install tooling is up to date python -m pip install -U pip wheel setuptools
• 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
• Browse to notebooks/{search_engine}/{collection}
• Open the appropriate notebook for your search engine, run each cell, and ensure you get a graph at the last cell:
  • "hello-ltr (Solr).ipynb"
  • "hello-ltr (ES).ipynb"
  • "hello-ltr (OpenSearch).ipynb"

Tests

Automatically run everything...

NB: It may be necessary to increase the number of open files on MacOS to a higher value than the default 256 for the tests to complete successfully. Use:

$ ulimit -n 4096

to increase the value to a sensible amount.

To run a full suite of tests, such as to verify a PR, you can simply run

./tests/test.sh

Optionally with containers rebuilt

./tests/test.sh --rebuild-containers

Failing tests will have their output in tests/last_run.ipynb

You can test one or more engines by specifying a comma delimited list: ./tests/test.sh --engines=solr,opensearch,elasticsearch

While developing...

For more informal development:

• Startup the Solr, OS, and ES Docker containers
• Do your development
• Run the command as needed: python tests/run_most_nbs.py
• Tests fail if notebooks return any errors
  • The failing notebook will be stored at tests/last_run.ipynb