RAG SQL Agent

RAG SQL Agent is a Retrieval-Augmented Generation (RAG) application designed to interact with SQL databases using natural language queries. It leverages advanced language models to generate SQL queries, retrieve relevant data, and provide human-readable answers.

Features

• Natural Language to SQL: Converts user questions into SQL queries.
• Database Interaction: Supports querying SQLite databases.
• Vector Database Integration: Uses ChromaDB for vectorized document storage and retrieval.
• Streamlit UI: Provides an intuitive interface for user interaction.
• Local and Remote LLM Support: Supports both local and remote Ollama servers for language model inference.

Key Directories and Files

• Code/: Contains the core application logic, including database interaction, LLM integration, and utility functions.
• DB/: Stores the SQLite database (sakila.db).
• Test_Files/: Includes unit and integration tests for the application.
• Vector_DB/: Stores vectorized representations of database tables for retrieval.

Installation

1. Clone the repository:

   git clone https://github.com/beckerfelipee/Rag-SQL-Gen-Agent.git
   cd Rag-SQL-Gen-Agent

2. Install dependencies:

   pip install -r requirements.txt

3. Set up the environment variables:

• Create a .env file in the root directory.
• Add the required variables (OLLAMA_SERVER, OLLAMA_LOCAL_SERVER).

4. Ensure the SQLite database is in the DB/ directory.

Usage

Run the Streamlit UI:

streamlit run Code/UI.py

Ask questions about the database and get answers in natural language.

License

This project is licensed under the MIT License.