Index Files and Folders with Indexly
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.
Category: Documentation
Database Analysis – Analyze SQLite Databases
Analyze SQLite databases with Indexly to extract table summaries, detect relationships, generate ER diagrams, and export structured insights in JSON, Markdown, or HTML.
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Semantic Indexing in Indexly – Overview
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.
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Chinook DB Examples – Real-World SQLite Analysis
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.
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The Story of Chinook – A Narrative SQLite Database Case Study
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.
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Indexly Logging System – NDJSON Standard and Legacy .log Support
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.
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Legacy .log Logging System – Full Documentation
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.
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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.
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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.