langframe 0.4.0

langframe is a Python DataFrame library for processing text data with APIs inspired by PySpark.

Author-email: typedef <[e-mail]> License: Apache-2.0 Requires-Python: >=3.10, <3.13 Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM Project-URL: Homepage, https://github.com/typedef-ai/langframe Project-URL: Issues, https://github.com/typedef-ai/langframe/issues

# langframe

langframe is a Python DataFrame library for processing text data, with APIs inspired by PySpark. It includes utilities tailored to working with language and special operations called semantic operators, which use large language models (LLMs) to batch-transform data.

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## 📁 Directory Structure

The repository is organized as follows:

`bash langframe/ ├── src/langframe/ # Core library │ ├── api/ # Public API (e.g., DataFrame, Column, functions) │ ├── _backends/ # Execution backends: local and cloud (Typedef) │ ├── _logical_plan/ # Logical plan representation for operators ├── rust/ # Rust crates compiled with Maturin ├── tests/ # Test suite structured to match the source layout `

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## 🛠️ Development Setup

Local development requires uv and a Rust toolchain.

### First-Time Setup

From the root of the repo:

`bash uv run maturin develop --uv `

This will:

• Create a virtual environment

• Build the Rust crate

• Install Python dependencies

• Set up the package in editable mode

> This command also places the built dynamic Rust library inside src/langframe.

### Making Changes

• To apply changes made to Python code:

  `bash uv sync `

• To apply changes made to Rust code:

  `bash uv run maturin develop --uv `

  Add –release or -r to build the Rust crate in release mode (better performance).

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## ✅ Running Tests

Run an individual test file:

`bash uv run pytest tests/path/to/test_foo.py `

Run all tests for the local backend:

`bash uv run pytest -m "not cloud" tests `

Run all tests for the cloud backend:

`bash uv sync --extra cloud # Installs cloud-specific dependencies uv run pytest -m cloud tests `

> ⚠️ Note: All tests require a valid OpenAI API key set in the environment variables.

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## 📓 Running Notebooks (VSCode / Cursor)

To run demo notebooks:

1. Install the Jupyter extension.

2. Add the .venv path to Python: Venv Folders in VSCode settings: - Open settings: Preferences: Open User Settings - Go to Extensions → Python → Python: Venv Folders

3. Open a notebook, select the correct kernel from your virtual environment, and run cells.

> Restart the kernel to reflect any code changes made to the langframe source.

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Have questions or want to contribute? Let us know!