Building a Safe AI Database Assistant with Azure OpenAI, LangChain & Function Calling
The article describes how to build an AI-powered data analyst using Azure OpenAI, LangChain, LangGraph, Function Calling, and SQLite. The assistant can analyze CSV data, query a SQL database safely, choose predefined backend functions automatically, and explain results clearly without hallucinations or unsafe SQL.
Why it matters
This approach demonstrates how to build a safe and explainable AI assistant for data analysis, addressing common issues with AI models and databases.
Key Points
- 1Addresses common problems with AI models and databases, such as hallucinated SQL queries and unsafe code execution
- 2Uses Azure OpenAI, LangChain, LangGraph, and Function Calling to build a safe and explainable AI assistant
- 3Loads COVID data into a pandas DataFrame and SQLite database, and exposes controlled computation via tools
- 4Enforces the use of pre-approved backend functions to avoid arbitrary code execution
Details
The article walks through the process of building an AI-powered data analyst that can answer analytical questions about COVID data. It starts by connecting to Azure OpenAI using LangChain, then creates a DataFrame Agent to perform controlled computations on the data. The dataset is then converted to a SQLite database, and a SQL Agent with LangGraph is used to query the data safely. Instead of letting the model generate SQL directly, the system uses pre-approved backend functions to ensure only allowed operations are performed. This approach avoids hallucinations, unsafe SQL execution, and lack of control over the model's access. The final system provides explanations for all results, making it a robust and trustworthy AI assistant.
No comments yet
Be the first to comment