LLMs and SQL
Francisco Ingham and Jon Luo are two of the community members leading the change on the SQL integrations. We’re really excited to write this blog post with them going over all the tips and tricks they’ve learned doing so. We’re even more excited to announce that we’ll be doing an hour long webinar with them to discuss these learnings and field other related questions. This webinar will be on March 22nd - sign up at the below link:
The LangChain library has multiple SQL chains and even an SQL agent aimed at making interacting with data stored in SQL as easy as possible. Here are some relevant links:
Most of an enterprise’s data is traditionally stored in SQL databases. With the amount of valuable data stored there, business intelligence (BI) tools that make it easy to query and understand the data present there have risen in popularity. But what if you could just interact with a SQL database in natural language? With LLMs today, that is possible. LLMs have an understanding of SQL and are able to write it pretty well. However, there are several issues that make this a non-trivial task.
The main issue that exists is hallucination. LLMs can write SQL, but they are often prone to making up tables, making up fields, and generally just writing SQL that if executed against your database would not actually be valid. So one of the big challenges we face is how to ground the LLM in reality so that it produces valid SQL.
