Conversational AI refers to technologies that enable humans to interact with machines using natural language, either through text or voice. This includes chatbots, voice assistants, and other types of conversational interfaces.
Conversational AI for SQL refers to natural language interfaces that enable users to interact with databases using everyday language instead of writing SQL code. This technology allows non-technical users to query and analyze data without requiring extensive SQL knowledge.
Some key aspects of conversational AI for SQL include:
Snowflake Cortex is a suite of machine learning (ML) and artificial intelligence (AI) tools integrated directly into the Snowflake Data Cloud. Cortex enables users to build, deploy, and manage ML models using Snowflake's scalable and secure infrastructure.
The era of conversational AI has revolutionized the way businesses interact with data. Snowflake Cortex Analyst, a cutting-edge platform, has taken this revolution to the next level by enabling text-to-SQL capabilities. In this article, we'll delve into the potential of Conversational AI for text-to-SQL using Snowflake Cortex Analyst and explore its benefits, applications, and implementation strategies.
Now that we have discussed the Conversational AI and Snowflake Cortex capabilities, let's delve into the implementation strategy. For the rest of this article, I will focus on creating a chatbot to answer business questions based on the data available in the Snowflake table as well as write the SQL queries on the go for the business users.
In this section, let's quickly make ourselves familiar with the data we will be using for the demo. I am focusing on daily revenue, cost of goods, and forecasted revenue for a sales organization.
The following query lets you explore the data table.
Having explored the data tables, in this section, we will be focusing on training the model with the existing data and sample queries. Cortex Analyst achieves this part with the help of a semantic file called YAML file. YAML files focus primarily focuses on the data tables, measures, dimensions, time dimensions, and verified queries mapped to the business questions.
For this demo, we would train the model to answer the following questions.
The following YAML code block would train the model to answer the above questions as well as write SQLs for the business users.
Once we have the YAML file ready, the next step is to upload the file into Snowflake using Snowflake's out-of-the-box features.
In this section, I will focus on designing a quick chatbot application using Python Streamlit. This application will act as an interface between the business users and the trained data models to answer critical business questions with simple English statements.
Now that we have the Data Model and Chatbot designed, let's try and monitor the results. If you are running this on your local machine, please install all the necessary Python libraries.
The following command would launch the Streamlit Chatbot application.
Conversational AI for text-to-SQL using Snowflake Cortex Analyst unlocks unprecedented potential for data-driven decision-making. By bridging the data gap and empowering non-technical users, businesses can:
