Text to SQL: Conversational Business Intelligence with LLMs

In today's data-driven world, the ability to quickly extract insights from complex databases is a valuable asset. Text-to-SQL technology bridges the gap between natural language and structured database queries, enabling users of all technical levels to interact with table data effortlessly.

Text to SQL by deepset is the natural language gateway to your structured data. Here's what you get when you use deepset Cloud to navigate and query your Snowflake data lake:

• Access data easily: Ask questions in plain English and get accurate SQL queries and results instantly. 
• Connect to Snowflake: Perform real-time data analysis without moving your data.
• Customize and optimize: Use any LLM in your text-to-SQL pipeline and tailor templates to fit your needs.
• Leverage expertise: Perform world-class text to SQL out of the box. Work with proven text-to-SQL pipelines and benefit from our experience.
• Ensure data security: Take advantage of strong guardrails that limit access to your Snowflake database for maximum security.

Benefit from deepset Cloud's superior text-to-SQL capabilities without moving your data out of your Snowflake data lake.

What is Text to SQL?

Large language models (LLMs) are good at processing unstructured text – think reports, news articles, or other document types. But in text to SQL, these models are used to extract insights from structured data, namely complex business intelligence (BI) tables. After we pass the database schema in the prompt, an LLM can use this information combined with its abilities to turn natural language into valid SQL and navigate the database on its own.

Text to SQL by deepset

Because of the unique challenges our customers face, we have often encountered the problem of using LLMs to query tabular datasets. As a result, we have done some of the most extensive research on LLMs for BI. Now, anyone can use deepset Cloud's text to SQL solutions, which transform natural language queries into precise SQL statements.

Of course, you can always manually review these LLM-generated SQL queries yourself, so you retain full control over the output your system produces.

Use cases

Text to SQL is designed to empower team members (with or without technical expertise) to generate sales reports, analyze customer trends, or track KPIs without writing complex SQL queries – significantly speeding the process of gaining insight from data. Other common uses include:

• Data exploration: Quickly explore hypotheses or identify patterns in large datasets by asking natural language questions of your data. For example, "Show me the top-selling products in each region over the last quarter."
• Ad hoc analysis: Enable data scientists and analysts to quickly prototype queries and explore data relationships without having to write and debug SQL for each new idea. This could involve questions like, "What's the correlation between marketing spend and customer acquisition in different demographics?"
• Multimodal question answering: Extract valuable information from both structured and unstructured data sources to answer complex user questions. For instance, "Compare our product reviews sentiment with sales figures for the past year."

deepset Cloud's Snowflake integration

Implementing text to SQL with deepset Cloud's Snowflake integration is straightforward and user-friendly. Here's how to get started:

To use deepset's Snowflake integration, all you need to do is log into deepset Cloud and select the text-to-SQL pipeline template that best matches your use case (remember that you can still adjust and extend it later). Then, you'll need to provide your Snowflake connection details and set up your prompt, in which you specify the schema used in your Snowflake database.

Text-to-SQL technology is changing the way organizations interact with their data. By combining the power of LLMs with structured database querying, deepset's solution delivers unprecedented ease of use, flexibility, and security. Whether you're a data analyst, business manager, or domain expert, text to SQL can help you realize the full potential of your Snowflake data lake.

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