AI Meets SQL - Developing AI Database Agent in 10 Minutes
AI Summary
Summary: Building an AI-Powered SQL Agent
- Introduction
- Creating an AI-powered SQL agent is straightforward today.
- The agent interprets English questions and executes SQL queries to return results.
- Setup
- Utilize a Jupyter notebook for development.
- Necessary tools: Docker, Python, and an OpenAI API key.
- Environment Configuration
- Install libraries for SQL connectivity (psycopg2) and LLM-specific libraries.
- Start environment setup in Jupyter notebook.
- Database Selection and Initialization
- Example uses PostgreSQL, but any SQL database is acceptable.
- PostgreSQL is run in a Docker container.
- Initialize the database and wait for it to be ready.
- Load sample data set and schema into the database.
- Create a read-only role for the SQL agent with SELECT privileges on specific tables.
- Agent Configuration
- Initialize OpenAI GPT model with an API key.
- Define agent’s role and provide hints about the dataset.
- Set model parameters, such as deterministic behavior (temperature set to zero).
- Agent Implementation
- Import required modules.
- Define OpenAI agent with API key and model behavior parameters.
- Initialize SQL database connection with read-only user credentials.
- Assemble LLM chain with OpenAI model, database, and fine-tuned agent behavior.
- Define a function to clarify the agent’s role and process user prompts.
- Testing the Agent
- Execute code snippet to take user prompts and generate SQL queries.
- Test with various requests, such as finding popular products or shoes in stock.
- Adjust output format to JSON if needed, including SQL query and response.
- Conclusion
- The SQL agent is capable of handling complex queries and returning results in different formats.
- Encouragement to access the Jupyter notebook and replicate the steps.
For more details, users can find the Jupyter notebook link in the video description and experiment with building their own SQL agent.