Indexly Overview

Indexly helps researchers and power users search Word, PDF, and text documents locally. Fast, offline, with tagging and FTS5.
Categories:
Tags:

“You who searches, finds.”

Welcome to Indexly – your fast, flexible, full-text local file search engine. Powered by Python and SQLite FTS5, Indexly brings powerful content searching, tagging, exporting, and indexing to your terminal.

Works great on Windows (tested), Linux, and macOS. CLI-only for now; GUI may come later.

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    E["🛠️ Developer Guide"]:::dev
    F["🏷️ Virtual Tag Detection"]:::tags
    G["🖥️ Customizing Windows Terminal"]:::terminal

    %% Links
    A --> B
    A --> C
    A --> D
    A --> E
    A --> F
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    B -->|Dev references| E
    C -->|Profiles & advanced filters| D
    C -->|Dev references| E
    F -->|CLI usage| D
    F -->|Developer tag extension| E

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Table of Contents

Key Highlights

  • Full-text search via SQLite FTS5
  • Regex & fuzzy search
  • Tagging & filtering
  • CSV analysis & stats
  • Watchdog real-time indexing
  • Export to PDF, TXT, JSON
  • Developer-friendly modular CLI

For full instructions, explore Usage Guide, Config & Features, or Developer Notes.

Requirements

  • Python 3.10+
  • Run locally, no server needed
pip install -r requirements.txt

Or manually:

pip install nltk pymupdf pytesseract pillow python-docx openpyxl rapidfuzz fpdf2 reportlab \
beautifulsoup4 extract_msg eml-parser PyPDF2 watchdog colorama

📌 See Installation Guide for Windows tips.

Workflow Overview 

flowchart LR
    A[Index files 📂] --> B[Search 🔍]
    B --> C[Filter & tag 🏷️]
    C --> D[Export results 🧾]

License & Credits

Author: N. K Franklin-Gent Built with ❤️ for the curious mind. Licensed under the MIT License.

Indexly Tagging System

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.

Indexly Usage Guide

A complete usage guide for Indexly. Discover installation steps, Windows Terminal setup, indexing, search, tagging, filtering, and exporting results in PDF, Markdown, or text formats.

Virtual Tag Detection — Examples & Tips

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.

Customizing Windows Terminal

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.

Indexly Configuration & Features

Learn how to configure Indexly for optimal performance. Discover search profiles, real-time indexing, tagging, caching, and CSV analysis to streamline data management.

Indexly Developer Guide

A complete guide for developers to explore Indexly’s architecture, modules, and build process. Learn how to extend search features, add filetype support, and contribute effectively.

Renaming Files with Patterns

Learn how to rename files in Indexly using smart patterns with dates, counters, and titles — safely preview changes using dry-run mode.

Minitab MTW files

Learn how to extract, decode, and analyze Minitab MTW files using Indexly’s extract-mtw feature — including optional extended metadata extraction from WorksheetInfo streams.

README

Database Update & Migration Utilities

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.

Analyze CSV: Visualize, Transform & Understand Your Data

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.

Cleaning CSV Data with Indexly

Automate CSV data cleaning in Indexly with intelligent type inference, datetime normalization, missing value imputation, and persistence. Ideal for data analysts and Python developers.