Text-to-SQL RAG with CodeLlama

#Text-to-SQL RAG with CodeLlama

Generate SQL queries from natural language using RAG (Retrieval-Augmented Generation) and CodeLlama models.

Features: RAG-enhanced generation, CodeLlama integration, Vector-based retrieval, Advanced prompt engineering

✅ Status: RAG system initialized successfully!

Question

Table Headers (comma-separated)

How It Works

RAG System: Retrieves relevant SQL examples from vector database
CodeLlama: Generates SQL using retrieved examples as context
Vector Search: Finds similar questions and their SQL solutions
Enhanced Generation: Combines retrieval + generation for better accuracy

Technology Stack

Backend: Direct RAG system integration
LLM: CodeLlama-7B-Python-GGUF (primary)
Vector DB: ChromaDB with sentence transformers
Frontend: Gradio interface
Hosting: Hugging Face Spaces

📊 Performance

Model: CodeLlama-7B-Python-GGUF
Response Time: < 5 seconds
Accuracy: High (RAG-enhanced)
Cost: Free (local inference)