Elastic Search Example
This project show how to use elastic search for a autocomplete select using relevance and full text search.
The elasticsearch server is served from docker with docker compose, to start execute:
docker-compose up -d
The example documents are in the sample-data directory, the models.json file include a sample list of models for brazilian car from (ComparaOnline)[comparaonline.com.br], to load this data in the elasticsearch server can be use the python script load_models.py so:
# change dir
cd data_loader
# install requirements
pip install -r requirements.txt
# run script
python load_models.py
Finally to test the results, is provided a express server, run with
# install dependencies
npm install
# run server
node index.js
open the sample on (http://localhost:3000/)
Configure search parameters
To configure the elasticsearch query edit the search.js file, the trade-off between full-text and relevance of every document (model) is controlled with the function_score parameter, the field_value_factor set how to get the score from the count_by_model of every document:
...
function_score: {
query: {
multi_match: {
query: term,
fields: ['brand', 'model'],
},
},
field_value_factor: {
field: 'count_by_model',
modifier: 'log1p',
factor: 2,
},
}
...
this configuration set the following final score:
new_score = old_score * log(1 + factor * count_by_model)