Metadata-Version: 2.4
Name: tablevault
Version: 0.2.0
Summary: Dataframe and File Transformation Execution and Versioning Tool.
Author-email: Jinjin Zhao <j2zhao@uchicago.edu>
License-Expression: MIT
Keywords: data,versioning,dataframe,python
Requires-Python: >=3.11
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: filelock
Requires-Dist: pyyaml
Requires-Dist: dataclasses_json
Requires-Dist: psutil
Requires-Dist: pydantic
Requires-Dist: click
Requires-Dist: rich
Provides-Extra: dev
Requires-Dist: ruff; extra == "dev"
Requires-Dist: pytest; extra == "dev"
Requires-Dist: mkdocs-material; extra == "dev"
Requires-Dist: mike; extra == "dev"
Dynamic: license-file

# Welcome to TableVault

TableVault is designed to manage data tables and artifacts in **complex and dynamic data workflows**. It promotes **data reusability** by capturing the full context of data transformations and ensuring atomic and transactional data states—transformations either clearly succeed or fail. It enhances **data interoperability** by easily connecting previous results with the configuration and input variables of subsequent transformations.


TableVault integrates with Python and can be used with popular data science libraries and tools, including Jupyter Notebooks, Pandas, NumPy, Transformers, and many others. The tool is particularly effective for workflows involving multiple dataframes, external artifacts (e.g., images, videos, documents), and **large language model executions**. TableVault is suited for **agentic pipelines** since one data-generating process can easily spin additional subprocesses.

**Explore: [Detailed Docs and Use Cases](https://j2zhao.github.io/tablevault/)**

Installation via pip:

```bash
pip install tablevault
```

This library is fully compatible with `Python>=3.11`.


## Quick Start

Check out [Basic Workflow](https://j2zhao.github.io/tablevault/workflows/workflow/) for a simple generic setup, and our CoLab examples [(1) Short Stories Q&A with OpenAI](https://colab.research.google.com/drive/1vHg5Vb8r1Zax2pKLOX6phPEuIDVhFctC?usp=sharing) and  [(2) GritLM Embeddings from Scientific Abstracts](https://colab.research.google.com/drive/1X4tFpPSfMnQ_Ch0nSNUTmiEcT0Eo40Uj?usp=sharing) for concrete use cases.
