tablegpt-agent 0.2.5

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TableGPT Agent

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Introduction

tablegpt-agent is a pre-built agent for TableGPT2 (huggingface), a series of LLMs for table-based question answering. This agent is built on top of the Langgraph library and provides a simple interface for interacting with the TableGPT model.

Installation

pip install tablegpt-agent

tablegpt-agent depends on pybox, which is a Python code sandbox delegator. pybox defaults to an in-cluster mode. If you want to run tablegpt-agent in a local environment, you need to install an optional dependency:

pip install pppybox[local]

Quick Start

Before using tablegpt-agent, ensure you have an OpenAI-compatible server set up to host TableGPT2. We recommend using vllm for this:

python -m vllm.entrypoints.openai.api_server --served-model-name TableGPT2-7B --model path/to/weights

Note: For production environments, it’s important to optimize the vllm server configuration. For details, refer to the vllm documentation on server configuration.

After setting up the server, you can use the following code to interact with the TableGPT model:

from datetime import date

from langchain_core.messages import HumanMessage
from langchain_openai import ChatOpenAI
from tablegpt.agent import create_tablegpt_graph
from pybox import LocalPyBoxManager


llm = ChatOpenAI(openai_api_base=YOUR_VLLM_URL, openai_api_key="whatever", model_name="TableGPT2-7B")

pybox_manager = LocalPyBoxManager()

agent = create_tablegpt_graph(
  llm=llm,
  pybox_manager=app_state.pybox_manager,
)

message = HumanMessage(content="Hi")
input = {
    "messages": [message],
    "parent_id": "some-parent-id",
    "date": date.today(),
}

async for event in agent.astream_events(
    input=input,
    version="v2",
):
    print(event)

Workflow

Main workflow

File Upload workflow

Code Execution

The tablegpt-agent directs tablegpt to generate python code for performing data analysis. This code is then executed within a sandbox environment to maintain system security. The execution is managed by the pybox library, which offers a straightforward way to run Python code outside the main process.

Plugins

Liscence

Model Card

See model_card.md.

Citation

If you find our work helpful, please cite us by

@misc{
}