- 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
sandboxfolder.
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 -dand 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