dbt-llm-tools aka. ragstar

LLM-based tools for dbt projects

dbt-llm-tools, also known as ragstar, provides a suite of tools powered by Large Language Models (LLMs) to enhance your dbt project workflow. It allows you to ask questions about your data and generate documentation for your models.

Here is a quick demo of how the Chatbot works:

https://www.loom.com/share/abb0612c4e884d4cb8fabc22af964e7e?sid=f5f8c0e6-51f5-4afc-a7bf-51e9e182c2e7

Key functionalities

• Chatbot: Ask questions about your data directly using the chatbot. It leverages your dbt model documentation to provide insightful answers.
• Documentation Generator: Generate comprehensive documentation for your dbt models, including descriptions and lineage information.

Getting Started

To install dbt-llm-tools with the UI:

1. Clone the repository:

   gh repo clone pragunbhutani/dbt-llm-tools

2. Navigate to the project directory:

   cd dbt-llm-tools

3. Install Poetry:

   make poetry

   • Add the poetry executable to your PATH and refresh the terminal.
4. Install the project dependencies:

   make install

5. Install an example project (optional):

   make fetch_example_project

6. Run the UI:

   make run_client

This will launch the client in your browser at http://localhost:8501/app.

Note: An OpenAI API key is required to use the tools.

Documentation

For detailed instructions and API reference, refer to the official documentation: https://dbt-llm-tools.readthedocs.io/en/latest/

Classes

• Chatbot:
  • Loads your dbt project information and creates a local vector store.
  • Allows you to ask questions about your data.
  • Retrieves relevant models and utilizes ChatGPT to generate responses.
  • Currently supports OpenAI ChatGPT models.

from dbt_llm_tools import Chatbot

# Instantiate a chatbot object
chatbot = Chatbot(
    dbt_project_root='/path/to/dbt/project',
    openai_api_key='YOUR_OPENAI_API_KEY',
)

# Step 1. Load models information from your dbt ymls into a local vector store
chatbot.load_models()

# Step 2. Ask the chatbot a question
response = chatbot.ask_question(
    'How can I obtain the number of customers who upgraded to a paid plan in the last 3 months?'
)
print(response)

• Documentation Generator:
  • Generates documentation for your dbt models and their dependencies.
  • Requires your OpenAI API key.

from dbt_llm_tools import DocumentationGenerator

# Instantiate a Documentation Generator object
doc_gen = DocumentationGenerator(
    dbt_project_root="YOUR_DBT_PROJECT_PATH",
    openai_api_key="YOUR_OPENAI_API_KEY",
)

# Generate documentation for a model and all its upstream models
doc_gen.generate_documentation(
    model_name='dbt_model_name',
    write_documentation_to_yaml=False
)

How it works

The Chatbot is based on the concept of Retrieval Augmented Generation and basically works as follows:

• When you call the chatbot.load_models() method, the bot scans all the folders in the locations specified by you for dbt YML files.
• It then converts all the models into a text description, which are stored as embeddings in a vector database. The bot currently only supports ChromaDB as a vector db, which is persisted in a file on your local machine.
• When you ask a query, it fetches 3 models whose description is found to be the most relevant for your query.
• These models are then fed into ChatGPT as a prompt, along with some basic instructions and your question.
• The response is returned to you as a string.

Partners

• JIIT's Open Source Developers Community