Unlocking Data Conversations: ChatGPT for Snowflake, dbt, and Streamlit
Table of Contents:
- Introduction
- Project Overview
- Prerequisites
- Setting up OpenAI and Snowflake Accounts
- Data Load and Modeling
5.1 Downloading the NYC Yellow Taxi Trip Dataset
5.2 Loading Seed Data and Creating Dimensions
5.3 Loading the Data Warehouse Layer
5.4 Creating DBT Models
5.5 Creating DBT Schema Files
- Setting up the Streamlit App
6.1 Docker Compose and Environment Setup
6.2 Configuring the Secrets File
6.3 Running the Streamlit App Container
- Program Logic and Functionality
7.1 Validating OpenAI and Snowflake Connections
7.2 Building Tables and Context
7.3 Interacting with the Streamlit App
- Conclusion
- FAQs
- References
Article
Introduction
Welcome to this tutorial on building a data engineering project using ChatGPT, DBT, Snowflake, and Streamlit. In this project, we will demonstrate how to leverage an external dataset, pass attributes to ChatGPT, Create a data model, and load data into Snowflake tables using DBT. We will then build a Streamlit app that allows end users to Interact with the data in Snowflake. Let's dive in!
Project Overview
The goal of this project is to create an application called SnowChat that utilizes ChatGPT, DBT, and Snowflake to enable users to query and receive responses Based on the data stored in Snowflake. This project will demonstrate the power and simplicity of leveraging AI technology to create a data-driven application.
Prerequisites
Before starting this exercise, there are a few prerequisites You need to Take Care of:
- Install Docker and Snowflake Developer Tools.
- Set up an account with Snowflake and OpenAI.
- Create an API key for OpenAI and a Snowflake user.
Setting up OpenAI and Snowflake Accounts
To create an OpenAI API Key, log in to your OpenAI account and navigate to the API Keys section. Generate a new API key specifically for Streamlit. Make sure to save the API key as we will need it later.
For the Snowflake side of things, there are two queries you need to run: one for setting up the DBT user and another for setting up the Streamlit user. These queries will create the necessary objects and grant access to them. Modify the queries as per your requirements, including setting a strong password and making any necessary naming convention changes.
Data Load and Modeling
To demonstrate the data engineering process, we will be using the NYC Yellow Taxi trip dataset. You can download the dataset from the provided link. Additionally, download the data dictionary from the NYC government Website, as it will assist us in building reference files for the next steps.
Once you have the dataset and data dictionary, we can proceed with loading the data into Snowflake. We will create seed files for reference data and load them into the seed schema. Then, we will load the seed information using DBT.
Next, we will create DBT models for Dimensions and facts. We will use the attributes from the dataset to generate the models and load the target Snowflake tables.
Lastly, we will create DBT schema files to update column and table descriptions within Snowflake. These files will be used by the Streamlit app to infer data when interacting with OpenAI.
Setting up the Streamlit App
To set up the Streamlit app, clone the SnowChat repository and navigate to the source/streamlit directory. Create a copy of the secrets.toml.template file and save it as secrets.toml. Fill in your OpenAI API key and Snowflake connection details in the secrets.toml file.
Now, start the Streamlit app container using Docker Compose. This will install the necessary dependencies and run the SnowChat app. Validate the connection to OpenAI and Snowflake by clicking the respective buttons on the app.
Program Logic and Functionality
The SnowChat app follows a specific logic. It initializes connections to OpenAI and Snowflake, builds table contexts based on the schemas, and generates tokens for OpenAI to understand user queries and respond accordingly. The app loops through schemas, reads tables and columns from the information schema, and employs SQL generation to interact with Snowflake.
During user interaction, the app infers the meaning of each column and Prompts the user for further queries. This mini data analyst app simplifies exploration and querying of data stored in Snowflake, allowing users to quickly find information.
Conclusion
In this tutorial, we have learned how to build a data engineering project using ChatGPT, DBT, Snowflake, and Streamlit. We explored the steps involved in setting up accounts, loading data, creating models, and building a Streamlit app. Embracing AI technology can enhance data-driven applications and enable intuitive interactions with data. Start building your own projects with these powerful tools!
FAQs
Q: Can I use a different dataset for this project?
A: Yes, you can use any dataset of your choice. Make sure to update the necessary steps accordingly.
Q: How can I modify the prompts and rules used in the SnowChat app?
A: The prompts and rules can be modified in the code files provided with the SnowChat repository. Explore the specific modules mentioned to make changes.
Q: Is Docker necessary for running the Streamlit app?
A: Yes, Docker is required to set up and run the Streamlit app container. It ensures a consistent and reproducible environment.
References