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llm-agent

LLM-related code.

Developing

  1. Checkout the code.
  2. Create/activate your Python environment of choice.
  3. Install uv: pip install uv.
  4. Install dependencies: uv pip install -r pyproject.toml.
  5. Install dev dependencies: uv pip install -r pyproject.toml --extra dev.
  6. Run pre-commit install to install pre-commit hooks.
  7. Configure your editor for realtime linting:
    • For VS Code:
      • Set the correct Python environment for the workspace via ctrl+shift+P > Python: Select Interpreter.
      • Install the Pylance and Ruff extensions.
  8. Make changes.
  9. Verify linting passes scripts/lint.sh.
  10. Verify tests pass scripts/test.sh.
  11. Commit and push your changes.

Run evaluations

Run pre-defined experiments to evaluate LLM-components and output detailed evaluation reports:

python evals/run_evals.py

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AI backend using LLMs and agents for the NOAA Knowledge Mesh NLP Prototype Project

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