TableZoomer

📖 Overview

TableZoomer is a sophisticated multi-agent framework designed for table question answering (TQA) tasks. It employs a ReAct (Reasoning and Acting) approach with multiple specialized agents working collaboratively to understand, analyze, and answer questions about tabular data.

🚀 Quick Start

Prerequisites

Our framework is built on the MetaGPT library.

Python 3.9 or later, but less than 3.12
metagpt >= 0.8.1
openai == 1.6.1
vllm (optional)

Basic Usage

1. Configure model interfaces and prompt templates.

• Create the config file in agent_config/. Example: agent_config/example.yaml

• Set the LLMs and prompt templates you prefer in the config file.

  • LLM config (Refer to agent_config/qwen3-8b_api.yaml):

    For close-sourced api, usage:

    llm:
    api_type: "openai"  # or azure / ollama / groq etc.
    model: "gpt-4-turbo"  # or gpt-3.5-turbo
    base_url: "https://api.openai.com/v1"  # or forward url / other llm url
    api_key: "YOUR_API_KEY"
    

    For the model employed as described in Model Deployment, usage:

    llm:
    api_type: open_llm
    base_url: "http://{ip}:{port}/v1"
    model: "{model}"
    temperature: 0
    calc_usage: false
    api_key: your_api_key
    

  • Prompt config Example prompts: prompts/*.txt

2. RUN

• Main program entrance: table_agent.py.

  Run table_agent.py with your table, query and agent config file.

  python table_agent.py
  

• Run on DataBench: databench_infer.py

  python databench_infer.py