Building LLM Knowledge Bases For Advanced SQL Chains

Organizations constantly seek ways to extract valuable insights from their data warehouses. However, writing complex SQL queries to fetch database information can be time-consuming and require specialized expertise.

But now, we can leverage LLMs to generate the SQL needed to extract insights. We can generate sophisticated queries based on natural language input using SQL knowledge bases and few-shot-prompting techniques. This approach empowers non-technical users to access and analyze data effectively, democratizing data exploration and insights.

In this tutorial, we'll leverage LangChain chains to extract insights from BigQuery. To store our SQL query examples, we'll utilize Redis, a fast and efficient in-memory data store. Redis will serve as our knowledge base, allowing us to store and retrieve example queries. By leveraging Redis, we can perform similarity searches to find relevant query examples based on user input, enhancing the accuracy and relevance of the generated SQL queries.

Throughout this tutorial, we'll explore various components of LangChain, such as the SQL query chain, few-shot prompt templates, and semantic similarity example selectors. To illustrate the practical application of our knowledge base, we'll tackle a common challenge in fetching analytics data from BigQuery: dealing with nested data using the UNNEST function.

The Colab Notebook