Version Update

📦 SimpliSQL v2.0 — Major Update

SimpliSQL v2.0 introduces a redesigned interface, private local AI assistance,
visual workflows, advanced data-processing tools, and faster DuckDB operations.

🤖 Local AI Assistant

• Private, offline AI powered by local GGUF models through llama-cpp-python
• No API keys, cloud account, or Ollama service required
• Natural-language SQL generation using the schemas of selected tables
• SQL explanation, correction, and optimization with DuckDB-aware guidance
• SQL and Python generation modes, plus automatic intent detection
• Data-science and machine-learning assistance in the Python notepad
• Streaming responses with stop and clear-chat controls
• Copy or insert generated code directly into the SQL/Python editor
• Query validation before generated SQL is applied
• Multiple model profiles, including TinyLlama, Gemma, Phi-2, and SQLCoder,
  plus automatic discovery of custom .gguf files
• Persistent AI settings and chat history

AI processing stays on the user's computer. A compatible GGUF model must be
placed in the models directory before the assistant can be used. Model speed,
memory use, and response quality depend on the selected model and hardware.

🎨 Core Features

• ✨ Modern UI redesign with dark and light themes and a professional gradient header
• 🔍 Advanced Search & Replace (Ctrl+F) with case-sensitive and whole-word options
• 📊 Enhanced data visualization with alternating row colors and improved performance
• ⚙️ Workflow Builder for visual, multi-step data-processing pipelines
• 🎨 Persistent theme system with quick dark/light mode switching
• 📋 Dashboard system for saved data views and aggregations

⚡ Performance Improvements

• ZIP archive support for loading supported files directly from archives
• Optimized DuckDB configuration with eight threads and a 16 GB memory limit
• Faster Parquet exports using direct DuckDB COPY operations
• Smart column detection that converts only problematic columns to VARCHAR
• Fast schema inspection without loading the complete dataset

🔄 Data Processing Enhancements

• Error-row tracking automatically saves rows skipped during CSV imports
• Batch processing for merging multiple CSV, Excel, XML, or JSON files
• Advanced column filters with contains, equals, comparison, and other operators
• Interactive pivot tables with configuration and preview support
• Visual table joins for combining multiple datasets
• File splitting by column values into separate Parquet files
• Improved type safety for numeric, date, and string columns

💻 SQL Query Features

• Real-time SQL validation without execution (Ctrl+Shift+V)
• Query templates for common SQL operations
• DuckDB function helper with descriptions and examples
• Run to Store to execute a query and save its results directly as CSV
• Automatic type casting for arithmetic operations and date functions

🖥️ User Experience

• Cell context menus for copying, exporting, viewing statistics, and filtering
• Expanded keyboard shortcuts, including Ctrl+F, F3, Ctrl+M, and Ctrl+Enter
• One-click error copying for easier troubleshooting
• Smart file selection with previews and column selection before loading
• Optional sorting for large datasets, including tables with more than 100,000 rows

⚙️ Workflow Management

Build reusable pipelines with these step types:

• Load Data
• SQL Query
• Filter
• Aggregate
• Join
• Transform
• Pivot
• Export

Additional workflow capabilities include transformation syntax validation,
workflow persistence, test row limits, and asynchronous execution with progress
tracking.

🛠️ Technical Improvements

• Improved PyInstaller compatibility and resource-path handling
• Non-blocking workflow and AI execution
• Efficient schema-only reads
• More reliable mixed-type handling
• Local AI model loading through llama-cpp-python

🎯 Recommended Use Cases

Choose the classic lightweight version if you need:

• A simple SQL querying tool
• Basic file conversion and querying
• Minimal dependencies
• A quick setup

Choose v2.0 if you need:

• Private AI-assisted SQL and data analysis
• Production-ready data processing
• Visual workflow automation
• Advanced filtering and visualization
• Batch processing and error tracking
• Saved queries, dashboards, and reusable workflows

⌨️ Useful Shortcuts

Shortcut	Action
Ctrl+F	Open Search & Replace
F3	Find the next result
Ctrl+Shift+V	Validate SQL
Ctrl+Enter	Execute the current query
Ctrl+M	Open the associated workflow action

🔐 Privacy

• Data and prompts remain on the local machine
• No cloud AI service or API key is required
• No AI telemetry is sent by SimpliSQL

Thank you for using SimpliSQL. Bug reports, feature requests, and feedback are
welcome through GitHub Issues and Discussions.

Download link Gemma-4 : https://huggingface.co/unsloth/gemma-4-E2B-it-GGUF/resolve/main/gemma-4-E2B-it-Q4_K_M.gguf?download=true

File_Naming_BugFix

In this new version, we have identified and fixed below issue

• The filename that is being used in the sql queries is sanitized
• Fix on user provides a custom name like /tmp/malicious, which replaces the absolute path instead of storing the file in self.doc_dir.

V.1.0

🧠 Simplisql – Visual SQL, Simplified
Simplisql is an intuitive SQL builder built with Python and PyQt6. It is designed to help analysts, engineers, and data enthusiasts create complex SQL queries for offline files like CSV, XLS, XLSX, txt, and XML.

✨ Key Features

🧩 Upload file to Table: Create table by just click and load

🛠️ Dynamic Inputs: You can use all Sql queries like CTE, Group by, join, filters, CASE logic, rank . etc

⚡ DuckDB Native: Built for speed, with support for Parquet.

🧬 Query save: You can save your queries and quick retrieve and run.

💾 Lightweight on disk : Simplisql stores and reads data using the Apache Parquet format – a modern, columnar storage format with Fast for reads and writes on in memory

🎨 Modern UI: Dark mode, interactive widgets, and a clean, distraction-free interface.

🧑‍💻 Built With

PyQt6

DuckDB

Pandas

PyArrow

🚀 Why Simplisql? SQL is powerful, no complex installation, plug and use, major offline formats supports , Simplisql lets you focus on what you want.

• TUTORIAL Video : https://youtu.be/sH_XHvJJqJ0?si=xZ4VSCmwwGFvm3RR

Perfect for:
want to use plug and play SQL for huge files (like 100Gb - Approx time to load is 8 to 10 minutes to parquet, and complex queries can be be as fast as 2 to 3 minutes)
Fast prototyping
Space Constraints
Building reproducible query workflows

PS : This currently Only supports windows

You can buy me a coffee for this effort : https://buymeacoffee.com/chandans