# Multi-Agentic SQL Generator

The Multi-Agentic SQL Generator is a modular, multi-step system that translates natural language queries into SQL queries, validates and optimizes them, and then executes them against a SQLite database. The system leverages OpenAI's language models (via a LangGraph workflow) for query understanding, validation, and optimization. It also provides evaluation capabilities using RAGAS metrics (such as context precision and context recall) to assess performance and quality.

## Capabilities

- **Natural Language Query Understanding:**  
  Converts natural language queries into structured SQL metadata according to a predefined database schema.

- **Query Validation:**  
  Checks the generated SQL for syntax errors and security risks (e.g., harmful operations like `DROP`, `DELETE`).

- **Query Optimization:**  
  Optimizes SQL queries for performance, ensuring only the necessary columns, joins, and filtering conditions are included.

- **SQL Execution:**  
  Executes the optimized SQL query against a SQLite database and returns the results.

- **Evaluation with RAGAS Metrics:**  
  Evaluates the generated output using metrics like:
  - **Faithfulness:** How closely the output matches the expected result.
  - **Answer Relevancy:** How well the result addresses the user query.
  - **Context Precision:** Whether the query returns only the necessary data.
  - **Context Recall:** Whether the query returns all required data.

- **Extensibility and Deployment:**  
  Easily integrable with front-end frameworks (e.g., Chainlit) and deployable on platforms like Hugging Face Spaces.

## Installation

1. **Clone the Repository:**

   ```bash
   git clone https://github.com/yourusername/multi-agent-sql-generator.git
   cd multi-agent-sql-generator