Tableau Langchain

Tableau tools for Agentic use cases with Langchain

This project builds Agentic tools from Tableau capabilities for use within the Langchain and LangGraph frameworks.

• query_data.py
  • Query a Published Datasource in natural language
  • Leverage the analytical engine provided by Tableau Headless BI
    • Only the data you need
    • Supports aggregations, filters and soon: calcs!
  • Scales securely by way of the Headless BI HTTP interface preventing SQL injection
• search_datasource.py
  • Search for relevant Published Datasources in natural language
  • Leverage thorough datasource metadata to find the right source of information
  • Datasources are curated by Stewards in Tableau
  • Clear semantics and models built for enterprise users sharpens the output of LLM queries

Tableau Headless BI supports a variety of use cases from UIs, to automation and in particular AI Agents. By providing a simplified JSON and HTTP interface to Tableau's underlying query engine, datasources that have passed the vetting process for clarity and usefulness in answering business questions become available to Agentic systems that can register said datasources as tools for reference and analysis. From data Q&A, to decision-making, report synthesis, data analysis and agentic automation the use cases are vast.

We welcome you to explore how Agentic tools can drive alignment between your organization's data and the day to day needs of your users. Consider contributing to this project or creating your own work on a different framework, ultimately we want increase the flow of data and help people get answers from data.

Getting Started

The easiest way to get started with running the headlesscopilot query pipeline is to try it in the jupyter notebook

Deploy to Heroku

Use this button to build and launch this app within Heroku. You will be prompted to set your environment (config) variables within Heroku, and then it will build and deploy the app for you. Once the app is running, you can navigate to the app page in Heroku and find the Settings tab. You can see the domain created for this app under the Domains section. In order to access the API, you can make a POST request like this:

Terminal Mode

These tools run on Python and its requirements are described in the environment.yml file which can be read by either Conda or Mamba to install packages. For help installing either of them, ask Perplexity for help and mention your operating system.

1 - Clone the repository

git clone https://github.com/Tab-SE/tableau_langchain.git

2 - Create a Python environment to run the code

2.1 - Using Anaconda or Miniconda

conda env create -f environment.yml

2.2 - Using Mamba

mamba env create -f environment.yml

3 - Activate your environment

3.1 - Using Anaconda or Miniconda

conda activate tableau_langchain

3.2 - Using Mamba

mamba activate tableau_langchain

4 - Duplicate file .env.template as .env and modify the values.

cp .env.template .env

5 - Run the chain in the terminal

5.1 - To run the chain interactively in the terminal

cd chains
python main.py

5.2 - To run the chain as an API service

cd chains
python main.py --mode api

6 - Type a question to the AI to see how it operates!