A natural language interface for querying SQL databases using LangChain and Groq LLM. This project allows users to interact with SQL databases conversationally, eliminating the need for SQL expertise.
Project Title: Chat with SQL DB
Domain: Generative AI | NLP | Natural Language to SQL | Data Analytics
Non-technical users often face challenges interacting with relational databases due to a lack of SQL knowledge. This project aims to bridge that gap by allowing users to query SQL databases using plain English.
An intelligent chatbot powered by LangChain and Groq’s LLM to:
- Accept natural language queries from users
- Translate them into SQL using LLM
- Execute the SQL query on SQLite or MySQL databases
- Display results in a conversational format
- Maintain context to support follow-up questions
- Support dynamic schemas and multi-database connections
- Create a chat-based web app to interact with databases using natural language
- Integrate LangChain’s SQL agent with Groq’s LLM
- Enable both local (SQLite) and remote (MySQL) database queries
- Ensure robust and context-aware interaction
| Tool / Technology | Purpose |
|---|---|
| Python | Backend logic and integration |
| LangChain | LLM-based SQL generation |
| Groq LLM | Fast natural language processing |
| Streamlit | Web interface (chat UI) |
| SQLite / MySQL | Databases |
| SQLAlchemy | Database abstraction layer |
| dotenv / .env | Environment variable management |
- 🧠 Natural language to SQL translation
- 🔁 Dual support for SQLite and MySQL
- ⏱️ Fast response time (~3 seconds)
- 🧩 Context-aware multi-turn conversation
- 🛡️ Robust error handling and fallback messaging
| Metric | Outcome |
|---|---|
| Query Accuracy | High (e.g., "List employees older than 50" translated correctly) |
| Response Time | < 3 seconds |
| Edge Case Handling | Graceful degradation with clear error messages |
| Robustness | Handled disconnections and missing credentials |