We will use a series of Amazon SageMaker notebooks to interact with an LLM and Amazon RDS database. The following diagram represents this post’s high-level architecture. Source CodeĪll the source code for this post’s demonstration is open-source and available on GitHub. We will also learn about the importance of using LangChain’s Prompt Template, Query Checker, few-shot promoting, and retrieval-augmented generation (RAG) to improve our results. In the following post, we will learn to use LangChain’s SQL Database Chain and SQL Database Agent with OpenAI’s text-davinci-003, an LLM instance within OpenAI’s GPT-3 series, to perform NLQ of an Amazon RDS for PostgreSQL database. For example, we ask questions like, “ How many customers have purchased in the last 12 months?” or “ What were the total sales of May?” These will be converted into SQL SELECT statements, like “ SELECT sum(amount) AS sales FROM purchases WHERE MONTH(purchase_date) = 5 AND YEAR(purchase_date) = 2023 ” The answer is then composed into textual explanation, such as “ A total of 384 customers made purchases in the last 12 months.”
Assuming the generated SQL query is well-formed, the query results will be converted into a textual explanation. Questions will be converted into SQL queries and executed against the database. Using LangChain’s SQL Database Chain and SQL Database Agent, we can leverage large language models (LLMs) to ask questions of an Amazon RDS for PostgreSQL database using natural language. It parses for keywords and generates relevant answers sourced from related databases, with results typically delivered as a report, chart or textual explanation that attempt to answer the query, and provide depth of understanding.”
To paraphrase analytics workflow product vendor YellowFin, “ Natural language query (NLQ), also known as natural language search, is a self-service business intelligence (BI) reporting capability that enables analytics users to ask questions of their data.
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