Intelligent Conversational Agentic RAG System
Problem
Complex database queries require technical SQL knowledge
Solution
Conversational AI that translates natural language to SQL
Impact
Democratizes database access for non-technical users
Users
Business analysts and data consumers
About the Project
An intelligent conversational AI system with chat context that converts natural language queries into SQL operations and generates multi-format outputs (text summaries, data tables, and visualizations) using advanced workflow orchestration. Built using Python, LangGraph, Azure OpenAI API, SQLite, Pandas, Matplotlib/Seaborn, with production-ready features including conversational memory, error handling, rate limiting, and SQL injection protection. The system demonstrates sophisticated natural language processing capabilities, enabling users to interact with databases through conversational interfaces while maintaining context across multiple queries.
Key Features
- Natural Language to SQL Conversion
- Conversational Memory
- Multi-format Output Generation
- Advanced Workflow Orchestration
- SQL Injection Protection
- Rate Limiting & Error Handling