Please read the documentation here to understand the concepts of Wren AI Service.

• Python 3.12.*, 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.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

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

• 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, Embedder or Document Store.

• Issues
• PRs