feat(csv-cleaning): implemented a fully automated CSV data cleaning pipeline with smart datetime parsing, mixed-type handling, missing-value imputation, and Rich-based summary display
feat(csv-cleaning): added delimiter fallback detection and dynamic visualization integration for clean and intuitive data exploration
feat(csv-cleaning): improved compatibility with numeric, categorical, and mixed-type columns, ensuring robust handling of nullable Pandas dtypes (Int64, Float64, object)
feat(csv-cleaning): integrated data cleaning with live chart previews (ASCII, Matplotlib, and Plotly) and summary export for downstream analytics
docs(cleaning): added comprehensive documentation, developer notes, and usage examples for the auto-clean pipeline
fix(csv-cleaning): resolved datetime format help string issue and enhanced scatter plot visualization behavior for diverse datasets
feat(csv-visualization): introduced unified visualization engine supporting numeric, categorical, and datetime axes with adaptive aggregation and clean auto-formatting
feat(csv-visualization): added automatic detection and conversion of Pandas dtypes, datetime strings, and category labels for seamless plotting
feat(csv-visualization): implemented dual rendering system (Plotly for interactive and Matplotlib for static/headless environments) with graceful fallback
feat(csv-visualization): added smart aggregation by mean for duplicate x-values and adaptive axis formatting for categorical, numeric, and datetime data
feat(csv-visualization): enabled high-quality exports to HTML, PNG, and SVG with automatic title and label generation
fix(csv-visualization): ensured robust handling of missing or mixed-value columns without crashes; improved data type coercion and visualization stability
docs(visualization): updated developer and user documentation to cover CSV visualization setup, supported chart types, and example pipelines
feat(db-update): added automatic backup and safe FTS5 database rebuild with schema validation and CLI integration for consistent metadata handling
refactor(db-update): relocated alias column from file_index to file_metadata and updated all dependent queries for schema clarity
feat(db-update): improved schema migration utility with timestamped backups, vacuum optimization, and detailed logging for safer updates
docs: optimized documentation for SEO and GitHub visibility; added structured metadata, improved keyword coverage, and refreshed README sections
meta: prepared project Indexly v1.0.4 release with unified CSV cleaning and visualization capabilities, enhanced database reliability, and improved developer experience