The purpose of the service is to find questions that are as similar as possible to the user's request.

Some requirements and thoughts are placed in approaches.md

Subproject for data downloading, EDA, embedding clustering, computing clusters centers.

To run data downloading you can follow commands:

cd clustering
python src/data/get_data.py --config_path params.yaml

To run embeddings computing run:

python src/data/create_embeddings.py --config_path params.yaml

To create ElasticSearch index with precomputed embeddings run (you need running ElasticSearch for this step):

python src/index/indexer_elastic.py --config_path params.yaml

You can specify configuration in params.yaml if you need

Service to create text embeddings via Tensorflow Universal Sentence Encoder

You can find the Swagger docs on http://localhost:5000/apidocs

Streamlit application to find nearest StackOverflow question.

The application needs ElasticSearch index to search by.

You can modify application params in search_app/params.yaml if you need

You can run any services in the single mode, see reference

To run all with docker-compose run

• Specify ElasticSearch user credentials in clustering/.env and /es.env files
• Run all containers with docker-compose up
• Create ElasticSearch index with command python src/index/indexer_elastic.py --config_path params.yaml

Finally, your search_app service will be deployed on localhost:8501

There is load test written with Locust in the tests/locustfile.py. To run test follow these steps:

• install locust with pip install locust
• go to tests folder
• run locust web UI with command locust
• open http://localhost:8089/ and specify test params (Number of users, Spawn rate, Host with running search server)
• start swarming

Also, you can run load tests without web UI, see Locust docs

Here you can see current load testing result with such hardware configuration:

• CPU: Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz 2.59 GHz
• RAM: 16 GB
• System disk space: 20 GB