The text2sql-workshop's intro from weet-ai

text2sql-workshop's Introduction

Text2SQL Workshop

This repo demonstrates the power of Large Language Models and Generative AI for simplifying access to data: instead of querying a database using SQL, why not doing so using Natural Language?
text2sql is a basic Python package which ships with Langchain. It contains simple logic for connecting to a local Postgresql instance, and by leveraging Langchain's create_sql_query_chain, it obtains metadata from our local DB instances and creates multiple prompts which are executed against an LLM (in our case, OpenAI ChatGPT).
As a result, we are able to convert questions from Natural Language to SQL Queries that are compliant with Postgresql's dialect.

Create a virtual environment with your tool of choice and install the text2sql Python package
Once the package is installed, you can create an IPython kernel and use it in Jupyter - checkout the notebooks provided in the sandbox folder.

from text2sql.core import Text2SQL

sql = Text2SQL(model = "gpt-3.5-turbo")
query = sql.query("How much do we have in total sales?")
print(query)

> SELECT SUM("Weekly_Sales") AS total_sales FROM sales

We use Docker to boot up a Postgresql DB. Just run docker-compose up -d and you should be good to go
To ingest data into Postgres, run text2sql/ingest.py (for simplification purposes, the package expects you to be running a local instance of Postgresql at port 5432)
Make sure that you properly set your OPENAI_API_KEY