🧠 DBPal — Chat Naturally with Your Database

Ask questions in plain English. Get instant SQL, visualizations, insights, and forecasts — no coding needed.

DBPal is an AI-powered Database Chat & Analytics Assistant built with Streamlit. Connect to any SQL database (PostgreSQL, MySQL, SQL Server), ask natural language questions like “Show me sales by region last month”, and get:

✅ Auto-generated SQL
✅ Interactive charts (Plotly/Altair)
✅ Anomaly & trend detection
✅ Forecasting (Prophet/ARIMA)
✅ Plain-English insights & “Ask Why?” root-cause analysis
✅ Export results to CSV/Excel
✅ Multi-LLM support (OpenAI, Anthropic, etc.) — pick your AI engine

Perfect for analysts, product managers, and non-technical users who want to unlock insights without writing a single line of SQL.

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✨ Features

• Natural Language to SQL — powered by LangChain + LLMs
• Auto-Visualization — smart chart recommendations based on data shape
• Anomaly Detection — Z-score, moving average, trend shifts
• Forecasting Engine — Prophet-based predictions with confidence intervals
• “Ask Why?” Drill-Down — automated root-cause analysis for metric changes
• Plain-English Summaries — “Top 3 products drive 68% of revenue”
• Multi-LLM Support — switch between OpenAI, Anthropic, local models
• SQL Transparency Toggle — see and learn the queries behind answers
• Secure Connection — credentials never logged or exposed
• Export Data — download results as CSV or Excel
• Sample Questions — get started instantly
• Advanced Mode Toggle — unlock JOINs, window functions, complex aggregations

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🛠️ Tech Stack

• Frontend: Streamlit
• Database Connectivity: SQLAlchemy, psycopg2, mysql-connector-python
• NL-to-SQL & AI: LangChain, OpenAI, Anthropic, LlamaIndex
• Analytics & Forecasting: Pandas, Prophet, Statsmodels
• Visualization: Plotly Express, Altair
• Caching & Performance: Streamlit cache, async analysis
• Security: Environment variables, input sanitization, query validation

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Project Structure

DBPal/
├── app.py                      # Main Streamlit application
├── advanced_analytics.py       # Core analytics engine
├── analytics_integration.py    # Database integration layer
├── analytics_prompts.py        # Pre-defined analytical prompts
├── requirements.txt            # Python dependencies
├── .gitignore                 # Git ignore rules
└── README.md                  # Project documentation

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🚀 Installation & Setup

Prerequisites

• Python 3.8+ (recommended: Python 3.10)
• Git for cloning the repository
• Database access (PostgreSQL, MySQL, or SQL Server)
• API keys for LLM providers (OpenAI, Anthropic, etc.)

Quick Install

1. Clone the repository

   git clone https://github.com/JV456/DBPal.git
   cd DBPal

2. Create virtual environment

   # Using venv
   python -m venv dbpal-env
   
   # Activate on Windows
   dbpal-env\Scripts\activate
   
   # Activate on macOS/Linux
   source dbpal-env/bin/activate

3. Install dependencies

   pip install -r requirements.txt

4. Launch the application

   streamlit run app.py

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📝 Example Usage and Output

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DBPal - Your intelligent database companion for advanced analytics