AI Tool for querying natural language on tabular data.
Self-hosted RAG framework with RBAC, multi-LLM support, and natural language SQL over tabular data
Ask questions about your business data in plain English, Get automatic SQL queries and visualizations, Receive AI-powered insights and recommendations, No SQL knowledge required
QueryWise: Natural-language SQL assistant with semantic context, glossary- aware query generation, and a React/FastAPI stack.
This project enables users to **generate SQL queries from natural language** using **LLM** of their choice while enforcing **Role-Based Access Control (RBAC)** and **Row-Level Security (RLS)**. It also includes **SQL injection detection** and **sensitive data logging** for compliance and security.
A hands-on RAG application that converts natural language questions into SQL queries for a real estate database. Built with LangChain, FastAPI, Streamlit, and OpenAI GPT-4o-mini. Perfect for learning Retrieval-Augmented Generation and LLM-powered database interactions.
🤖 DataWhisper is a system that translates natural language queries into SQL using an intelligent agent-based architecture. These agents work together to identify the relevant tables, generate SQL queries, execute the queries, and ultimately provide insights. It can be used with large-scale databases.
Self-correcting AI agent for natural language to SQL using HuggingFace smolagents and ReAct framework
tabletalk is a declarative language for seamless interaction with your database, enabling you to define data access configurations in a YAML file and query their data lakes using natural language
MCP agent translating natural language questions into explainable, reproducible data insights with built-in quality checks and semantic layer integration.
A context-aware chatbot backend powered by Django and LLMs, supporting multi-turn conversations, SQL generation, and optional visual similarity search with FAISS.
A modern MERISE database modeling tool built with Python and PySide6. Create MCD diagrams, generate MLD views, and export PostgreSQL SQL scripts.
REAR is a fast, LLM-free framework for multi-table retrieval that separates semantic relevance from structural joinability. By retrieving relevant tables, expanding with joinable ones, and refining noisy candidates, it consistently improves multi-table QA and Text-to-SQL performance—matching LLM-based methods at much lower cost and latency.
Structured Text-to-SQL pipeline combining LLM sketch generation, schema-aware two-phase retrieval, and validation-driven refinement. Improves SQL controllability and correctness via schema embeddings, join-path constraints, and minimal-edit correction. Research-oriented, fully traceable POC on MiniDev / Spider-style datasets.
Ask questions about your business data in plain English, Get automatic SQL queries and visualizations, Receive AI-powered insights and recommendations, No SQL knowledge required
🧠 Schema-Aware Natural Language to SQL Agent with Fine-tuned T5 Models
Simple example of SQL generation for a RAG pipeline
Production-ready intelligent knowledge management system built with LlamaIndex. Enables organizations to query, analyze, and extract insights from multiple data sources (documents, databases, APIs) through natural language using advanced RAG capabilities, multi-tenant architecture, and specialized query engines: SQL generation /intelligent routing.
A set of Python functions allowing for the generation of sql statements in Python.
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