How We Implemented a Chat-Like Reporting System Using LLMs

Creating a real-time reporting system that responds to complex requests through a chat-like interface posed several unique challenges. Our primary objective was to build a system that could understand user queries, handle conditional logic, dynamically generate SQL statements, and validate these statements before running them on production databases—all within seconds. Below is an in-depth look at how we implemented this solution, leveraging Large Language Models (LLMs) and state-of-the-art technologies to achieve a seamless user experience.

What Were the Main Objectives?

We wanted a system that:

Enables users to interact through text or voice commands.
Processes natural language queries and converts them into optimized SQL statements.
Handles conditional logic like "if," "else," and "when" to create more complex queries.
Validates and executes queries in real-time to generate instant reports.
Provides instant feedback and error notifications when issues arise.

The overarching goal was to replace traditional, static reporting tools with a more intuitive, conversation-based system that users could leverage for real-time data insights.

What Was Our Approach?

To build a robust system, we structured our solution around a few core principles: user input flexibility, dynamic query generation, real-time error handling, and fast reporting. Here’s how we tackled each component:

User Interaction Interface:
- We began by designing a chat-like user interface that accepts both text and voice inputs, giving users the freedom to interact in the way that suits them best. The voice recognition module, integrated with Google Speech-to-Text, converts spoken commands into text.
- The interface supports multi-turn conversations, allowing users to refine or build on previous queries. For instance, a user might start by asking, “Show me the sales report for Q1,” and then follow up with, “Only include regions where revenue exceeded $1 million.”
Leveraging LLMs for Natural Language Understanding:
- We utilized OpenAI’s GPT-4 model to understand and interpret user commands. Using a framework like LangChain, we built structured prompts and logic handlers that helped the LLM comprehend different types of requests.
- For example, if the user said, “Give me a breakdown of expenses if marketing costs exceeded $10,000,” the system could dynamically generate the corresponding SQL query:
```
SELECT * FROM expenses WHERE category = 'Marketing' AND cost > 10000;
```
- This LLM-driven approach ensures that even non-technical users can communicate with the system as they would with a colleague, without needing to know SQL or programming concepts.
Dynamic SQL Query Generation:
- Once the user’s intent is understood, the system translates natural language inputs into optimized SQL queries using pre-defined templates and dynamic token replacements. For complex queries involving multiple conditions or nested logic, we created a set of rules that help break down statements into smaller SQL components.
- An example would be:
  “Fetch the sales data for the top 5 performing products in Q1, excluding items with a return rate above 5%.”
  This query would be dynamically translated to:
```
SELECT * FROM sales WHERE quarter = 'Q1' AND return_rate <= 5 ORDER BY sales DESC LIMIT 5;
```
Real-Time Query Validation:
- Before running any SQL query, the system performs a real-time validation check. We implemented this step to prevent common issues like SQL injection, logical errors, and syntax issues that might arise from complex user inputs.
- Using Python’s pyodbc library, we connect to production databases, but a sandbox environment is used first to test the validity of queries. This prevents any accidental impact on live data and ensures that the results returned are reliable.
Instant Feedback Loop:
- If a query is malformed or logically incorrect, the system immediately notifies the user through the same chat interface. For example, if a user requests a report that involves non-existent columns, the system would respond with a message like, “The column ‘revenue_growth’ does not exist in the current dataset. Please check your input and try again.”
- This feedback mechanism helps users refine their requests quickly, reducing the need for back-and-forth troubleshooting.

How Did We Optimize Performance and Cost?

To make the system viable for high-traffic production environments, we optimized LLM interactions and resource utilization in the following ways:

Efficient Prompt Management:
- We created templates for common query patterns, reducing the time spent on repetitive LLM requests.
- We also structured prompts in a way that minimizes token usage, which lowered the overall cost of using the LLM for frequent queries.
Containerization and Scalability:
- Using Docker, we containerized the entire system, making it easy to deploy and scale across different environments.
- Each component, such as the LLM handler, SQL validator, and reporting module, runs as a separate microservice. This modular approach allows us to scale specific components independently based on demand, further optimizing resource usage.
Caching Mechanism:
- Implemented caching for frequent queries, so users get instant responses for common reports without re-running SQL each time.
- We stored these results in MongoDB, which serves as a quick-access cache layer for repetitive requests.

What Was the Final Outcome?

By combining advanced language models, real-time SQL validation, and an intuitive chat-based interface, we created a highly responsive reporting system that significantly reduces the time and effort required for data analysis. Users can now generate complex reports and gain insights in seconds, all through a natural, conversational interaction.

This solution has set a new standard for production reporting, demonstrating how AI-powered systems can transform traditional workflows into more efficient, user-friendly experiences.

Reference

OpenAI Documentation