AI Agent for Text2SQL: The Magical Recipe for Natural Database Interactions

Have you ever dreamed of talking to your database like you’d chat with a colleague? The rise of text-to-SQL solutions has brought us excitingly close to this reality – a world where business users can gain insights from their data without writing a single line of SQL code.

As an AI developer working at the intersection of language models and databases, I’ve observed a fascinating transformation in how we interact with data. When ChatGPT burst onto the scene, one of my clients asked a seemingly simple question: “Could we connect ChatGPT to our databases for natural language queries?” While many might reach for a RAG (Retrieval-Augmented Generation) pipeline, I discovered that building a dedicated AI agent would yield far more powerful results.

Join me as I share my journey of creating a robust Text2SQL AI Agent using LangChain and LangGraph – a solution you can adapt for your own projects.

 

The Power of AI Agents

In the rapidly evolving AI landscape, AI agents stand out as game-changers. Harrison Chase, co-founder of LangChain, offers a compelling definition:

“An AI Agent is a system that uses an LLM to decide the control flow of an application.”

This isn’t just another buzzword. AI agents surpass simple text completion – they’re decision-making engines that orchestrate complex workflows. When you feed an input to a Large Language Model (LLM), it doesn’t just predict the next word; it makes strategic decisions about actions and pathways to achieve your goal.

 

 

The Four Pillars of Success

Every good AI agent is built on four fundamental pillars:

1. The Brain (LLM)

• Powers decision-making and understanding
• In our case, we’re using Azure OpenAI’s GPT-4
• Adaptable to other models like DeepSeek R1

2. Planning

• Strategizes the sequence of actions needed
• Breaks down complex queries into manageable steps
• Determines the optimal approach for each query
• For example, when asked about sales data:
  • First, identify relevant tables (customers, sales, employees)
  • Then, determine necessary joins
  • Finally, plan aggregations and filtering

3. The Tools

• The agent’s practical capabilities
• For our SQL agent, includes:
  • Database table listing
  • Schema information retrieval
  • Query validation
  • Query execution

4. The Memory

• Enables contextual understanding
• Maintains conversation state
• Facilitates natural dialogue about data
• Remembers previous queries for context

 

To illustrate how these pillars work together, let’s analyse a real example with the question: “Which customers spent the most in each country?”

1. The LLM processes the question and understands:

• We need to find customer spending.
• Group by country.
• Identify the highest spenders in each group.

2. Planning comes into play:

• It needs to identify tables containing customer, invoice, and location data.
• It will require aggregating spending by customer.
• It must group results by country.
• It needs to rank customers within each country group.
• It plans the sequence: customer location → purchase data → aggregation → ranking.

3. Tools execute in sequence:

• First, it lists available tables.
• It retrieves schema information for relevant tables (e.g., Customer, Invoice).
• It constructs and validates the query (iterating and improving until a valid query is generated).

4. Memory stores:

• The tables used.
• The relationship between customers and invoices.
• The fact that we are interested in customer spending.
• This context helps with follow-up questions like “Show me their recent purchases.”

 

Building the Magic

Let’s dive into the implementation. We’ll use LangChain for core functionality and LangGraph for workflow orchestration. Here’s our agent’s foundation:

The real magic happens in our tools’ implementation:

 

The Agent at Work

When a user poses a question like “Which sales agent made the most in sales in 2009?”, our agent springs into action:

1. 1Surveys the database landscape (lists available tables)
2. Understands the data structure (retrieves schema information)
3. Crafts and validates the perfect SQL query
4. Executes the query and presents results in plain English

 

Powerful Features, Real Benefits

Our implementation delivers several key advantages:

1. Rock-Solid Security: Query validation prevents SQL injection and errors
2. Adaptable Design: Works with various databases and schemas
3. Easy Maintenance: Clean, modular code structure
4. Contextual Intelligence: Maintains conversation history
5. Natural Interaction: Understands plain English queries

 

Real-World Impact

This Text2SQL agent transforms various business scenarios:

• Business Intelligence: Democratizes data access for non-technical users
• Data Analysis: Simplifies complex query construction
• Customer Service: Enables quick, accurate data retrieval
• Database Administration: Streamlines routine operations

 

See It in Action

I’ve prepared a demonstration that showcases our agent handling various queries:

1. “Show me the top 5 best-selling tracks of all time”
2. “What was the total revenue for each genre in 2009?”
3. “Which customers spent the most in each country?”

 

 

The demo highlights how we’ve bridged the gap between technical SQL capabilities and user-friendly interaction, making database querying accessible to everyone in your organization.

 

Conclusion

Building an efficient Text2SQL AI Agent isn’t just about connecting an LLM to a database – it’s about carefully orchestrating the interaction between language understanding, tool capabilities, and memory. Through LangChain and LangGraph, we’ve created a solution that makes database interactions feel natural and accessible.

At hiberus, we are ready to help you implement AI in your organization. Our expertise in generative AI allows us to design personalized solutions that drive your business toward the future. Have questions or ideas for improvements? Let’s connect and collaborate! 

Contact us to discover how AI can revolutionize your business!