agentic_librarian

An agentic book recommender system

Development Environment

Docker Setup

Build the Docker image:

docker build -t agentic-librarian .

Run the container:

docker run -it agentic-librarian

Dependencies

This project uses uv for fast dependency management. Dependencies are defined in pyproject.toml.

To install dependencies:

# Install uv
curl -LsSf https://astral.sh/uv/install.sh | sh

# Install project dependencies
uv pip install -e .

# Install development dependencies
uv pip install -e ".[dev]"

The development environment includes the following packages:

• ML/Data Science: mlflow, pandas, scikit-learn, PyTorch
• API/Web: requests, google-api-python-client, firecrawl-py
• Audio/Books: audible
• LLM: openai, langchain, langchain-openai

Code Quality and Testing

This project uses pre-commit to ensure code quality and consistency. The following checks are run on every commit:

• Linting & Formatting: Handled by ruff (fastest Python linter/formatter).
• Static Analysis: Standard pre-commit-hooks (whitespace, YAML, etc.).
• Tests: pytest is run on a subset of fast, non-API-dependent tests.

Local Setup

1. Install pre-commit:

   pip install pre-commit

2. Install the git hooks:

   pre-commit install

3. (Optional) Run on all files:

   pre-commit run --all-files

Testing Strategy

• Fast Tests: Run automatically on commit.
• Slow/API Tests: Skipped in pre-commit to maintain speed. Mark these in code using:

  @pytest.mark.api_dependent
  def test_something_with_api():
      ...
  
  @pytest.mark.slow
  def test_something_slow():
      ...

• Manual Test Execution:

  # Run all tests
  pytest
  
  # Run only fast tests (what pre-commit runs)
  pytest -m "not api_dependent and not slow"