Python 3.12.0, recommended to use pyenv to manage the Python versions
install poetry with version 1.8.3: curl -sSL https://install.python-poetry.org | python3 - --version 1.8.3
execute poetry install to install the dependencies
copy .env.example file to .env, and .env.dev.example file to .env.dev and fill in the environment variables
[for development] execute poetry run pre-commit install to install the pre-commit hooks and poetry run pre-commit run --all-files to run the pre-commit checks at the first time to check if everything is set up correctly
[for development] to run the tests, execute make test

Start the service for development

The following commands can quickly start the service for development:

make dev-up to start needed containers
make start to start the service
- go to http://WREN_AI_SERVICE_HOST:WREN_AI_SERVICE_PORT(default is http://localhost:5556) to see the API documentation and try them.
- go to http://WREN_UI_HOST:WREN_UI_PORT(default is http://localhost:3000) to interact interact from the UI
make dev-down to stop the needed containers

Production Environment Setup

copy .env.prod.example file to .env.prod and fill in the environment variables
make build to build the docker image
make up to run the wren-ai-service and other containers
make down to stop the docker container

install psql
fill in environment variables: .env.dev in the src folder and config.properties in the src/eval/wren-engine/etc folder
start the docker service
evaluation
- make eval pipeline=ask args="--help"
- make eval pipeline=ask_details args="--help"
make eval_visualzation to compare between the evaluation results
to run individual pipeline: poetry run python -m src.pipelines.ask.[pipeline_name] (e.g. poetry run python -m src.pipelines.ask.retrieval_pipeline)

to evaluate the speed of the pipeline, you can enable the timer
- add environment variables ENABLE_TIMER=1 in .env.dev
- restart wren ai service
- check the logs in the terminal
to run the load test
- setup DATASET_NAME in .env.dev
- adjust test config if needed
  - adjust test config in pyproject.toml tool.locust section
  - adjust user count in tests/locust/config_users.json
- in wren-ai-service folder, run make dev-up to start the docker containers
- in wren-ai-service folder, run make start to start the ai service
- run make load-test
- check reports in /outputs/locust folder, there are 3 files with filename locust_report_{test_timestamp}:
  - .json: test report in json format, including info like llm provider, version
  - .html: test report in html format, showing tables and charts
  - .log: test log

go to the demo folder and run poetry install to install the dependencies
in the wren-ai-service folder, open three terminals
- in the first terminal, run make dev-up to start the docker container
- in the second terminal, run make start to start the wren-ai service
- in the third terminal, run make demo to start the demo service
ports of the services:
- wren-engine: ports should be 8080
- wren-ai-service: port should be 5556
- wren-ui: port should be 3000
- qdrant: ports should be 6333, 6334

Please read the documentation here to check out how you can add your preferred LLM or Document Store.