AutoDoctor Documentation
Official AutoDoctor documentation for users, technical operators, and developers. Covers installation, configuration, diagnostics, remediation, API, dashboard, and troubleshooting.
Welcome to the Indexly documentation hub.
Indexly is a local-first CLI for indexing, searching, analyzing, and organizing files without sending your data to external services.
flowchart LR
A["Install (pip or Homebrew)"] --> B["Index Local Files"]
B --> C["Search / Regex"]
C --> D["Tag, Organize, and List"]
D --> E["Analyze Data (CSV/JSON/DB)"]
E --> F["Compare, Backup, Restore"]
F --> G["Observe, Doctor, and Maintain DB"]
| Goal | Recommended Page |
|---|---|
| Install and verify on Windows, macOS, Linux | Install Indexly |
| Learn command workflows end-to-end | Usage Guide |
| Improve indexing quality and ignore rules | Ignore Rules & Index Hygiene |
| Organize folders and inspect logs | Organizer, Lister |
| Analyze CSV/JSON/XML/SQLite datasets | Data Analysis Overview |
| Run statistical inference for CSV datasets | Inference Docs |
| Compare files and folders safely | File & Folder Comparison |
| Maintain health and schema consistency | Indexly Doctor, DB Migration Utility |
| Extend or contribute to the project | Developer Guide |
If you are contributing code, start with:
indexly show-help --details for parser-level command scopeIndexly is licensed under the MIT License.
Official AutoDoctor documentation for users, technical operators, and developers. Covers installation, configuration, diagnostics, remediation, API, dashboard, and troubleshooting.
Understand why Indexly’s semantic-aware indexing makes search reliable, fast, and human-friendly.
Practical Indexly usage guide for Windows, macOS, and Linux. Covers indexing, search, regex, tagging, analysis, organizing, backup/restore, and common troubleshooting.
Learn how to use Indexly’s powerful file tagging system to categorize, organize, and search files effortlessly. Supports bulk tagging, recursive folder tagging, and instant tag lookups.
Learn how Indexly detects virtual tags from documents using customizable regex rules. Includes practical examples, editable fields in fts_core.py, and tips for refining OCR-based tag extraction.
Learn how to fully customize Windows Terminal for productivity and aesthetics. This step-by-step guide covers installing Chocolatey, Scoop, Oh My Posh, Neovim, PowerShell modules, fzf, and fonts to build a powerful Linux-like development environment on Windows. Created collaboratively with ChatGPT.
Learn how to configure Indexly for optimal performance. Discover search profiles, real-time indexing, tagging, caching, and CSV analysis to streamline data management.
Learn how to develop Indexly safely and efficiently. Covers project structure, optional dependency design, command wiring, quality checks, and Homebrew-friendly packaging practices.
Learn how to rename files in Indexly using smart patterns with dates, counters, and titles — safely preview changes using dry-run mode.
Learn how to extract, decode, and analyze Minitab MTW files using Indexly’s extract-mtw feature — including optional extended metadata extraction from WorksheetInfo streams.
Automatically organize files by date, name, or extension with full logging, backups, duplicate detection and audit support using Indexly Organizer.
Learn how to index files and folders with Indexly using simple CLI commands. Filter by file type, enable advanced extraction, and keep your index up to date automatically.
Install Indexly on Windows, macOS, and Linux with clear steps for pip and Homebrew. Includes verification, optional feature packs, and troubleshooting.
Safely organize and classify files using intelligent profiles with full logging, hashing, audit trails, dry-run planning, and automation support in Indexly Organizer.
Learn how Indexly analyzes CSV, JSON, NDJSON, XLSX, XML, YAML, and Parquet files using its universal loader, orchestrator, and smart pipelines.
Analyze SQLite databases with Indexly to extract table summaries, detect relationships, generate ER diagrams, and export structured insights in JSON, Markdown, or HTML.
Use Indexly Lister to analyze organizer logs, filter files by extension, category, date, and detect duplicates with zero risk.
Understand why semantic indexing exists in Indexly, how it fixes real-world search relevance issues, and how rule-based semantic filtering improves results in large local databases.
Indexly Doctor is a comprehensive diagnostic and repair tool that inspects your environment, configuration, and database health, and can automatically apply safe fixes when needed.
Incremental, encrypted backups with automatic scheduling and reliable restore chains in Indexly.
Explore how Indexly analyzes the Chinook sample database: table summaries, relationships, ER diagrams, and exported Markdown reports from a real-world multi-table SQLite database.
Explore the Chinook sample database through a narrative lens. Understand tables, relationships, and real-world data structure while seeing how Indexly brings SQLite databases to life.
Semantic observers let Indexly detect meaningful file changes, not just filesystem events.
Learn how to safely update, migrate, and manage your Indexly database schema and FTS5 tables without losing data. Includes full CLI examples and explanations of key differences between normal and FTS5 tables.
Compare files and folders using Indexly with GitHub-style diffs, similarity scoring, context folding, and JSON output.
Understand Indexly’s logging architecture, including the modern NDJSON-based logging system and legacy .log support. Learn how logs are structured, rotated, analyzed, and migrated.
Complete documentation of Indexly’s legacy .log-based logging system. Learn how classic log files are parsed, cleaned, normalized, exported, and migrated to the modern NDJSON logging standard.
How Indexly detects, prepares, resamples, and visualizes time-series data using Plotly and Matplotlib. A complete guide to frequency conversion, rolling windows, dual-axis handling, and statistical considerations.
Explore, visualize, and normalize CSV datasets in Indexly using statistical summaries, skew detection, and ASCII visualizations. Perfect for data analysts and developers working with terminal-based data exploration.
Automate CSV data cleaning in Indexly with intelligent type inference, datetime normalization, missing value imputation, and persistence. Ideal for data analysts and Python developers.