This project provides a pre-built agent for TableGPT2, a series of LLMs designed for table-based question answering. It offers a user-friendly interface for interacting with TableGPT2, leveraging the Langgraph library, and includes evaluation scripts for table-related benchmarks.

How It Works

The agent is built on the Langgraph library, enabling a structured and stateful approach to LLM agent development. This framework facilitates the creation of complex agentic workflows by defining nodes and edges, allowing for modularity and easier management of conversational state and tool usage.

Quick Start & Requirements

• Primary install: pip install tablegpt-agent
• Prerequisites: Access to TableGPT2 models (e.g., via Hugging Face).
• Documentation: https://tablegpt.github.io/tablegpt-agent/
• Model Card: TableGPT2 Model Card

Highlighted Details

• Pre-built agent for TableGPT2, simplifying interaction with tabular LLMs.
• Built on Langgraph for robust agentic workflow management.
• Includes evaluation scripts and datasets for table-related benchmarks in realtabbench.

Maintenance & Community

• The project is associated with the TableGPT2 research effort.
• Citation details are provided via BibTeX for academic referencing.

Licensing & Compatibility

• License: Apache 2.0.
• Compatibility: Permissive license suitable for commercial use and integration into closed-source projects.

Limitations & Caveats

The agent's functionality is dependent on the availability and performance of the underlying TableGPT2 models. Specific hardware requirements for running these models are not detailed in the README.