- 🔍 Overview
- ✨ Core Features
- 🚀 Getting Started
- 🧱 Prerequisites
- 🔧 Installation
- 🧪 Testing
- 📈 Usage
- 🙌 Contribution
- 📬 Contact
AIQueryNet is an innovative open-source system designed to transform natural language queries, including voice inputs, into precise SQL queries using Retrieval-Augmented Generation (RAG). Leveraging Whisper for speech recognition, sentiment analysis for intent understanding, and Pinecone vector embeddings for semantic search, AIQueryNet delivers accurate and context-aware database interactions.
This project consists of two main repositories:
-
🗣️ Voice Support with Whisper
Convert spoken queries into text using OpenAI's Whisper model for seamless voice-to-SQL interactions. -
🔐 Sentiment Analysis for Intent Detection
NLP-powered sentiment analysis enhances query understanding by capturing user intent. -
⚙️ Pinecone Vector Embeddings
Boosts query relevance with semantic search and intent-aware vector retrieval for precise schema mapping. -
💻 Natural Language to SQL
Transforms natural language inputs into accurate SQL queries, simplifying database interactions. -
⚒️ Scalable RAG Architecture
Combines retrieval and generation for context-aware, efficient query processing.
Get AIQueryNet up and running in minutes by cloning the repositories and setting up the backend and frontend.
Ensure the following are installed:
- Python (>=3.8)
- Node.js
- NPM
- Pinecone account and API key
- OpenAI API key (for Whisper and embeddings)
git clone https://github.com/mrshivamshaw/aiquerynet-backend.git cd aiquerynet-backend
pip install -r requirements.txt
export PINECONE_API_KEY='your-pinecone-api-key' export OPENAI_API_KEY='your-openai-api-key'
fastapi run app.py