The LLM Tagger plugin enhances note organization in Obsidian by using locally running large language models via Ollama to automatically generate relevant tags. It processes notes efficiently, avoiding re-tagging unchanged files, and can create brief summaries alongside the generated tags. Users can customize their tag list for more focused categorization and select different LLM models for processing. The plugin also supports an auto-tagging feature that applies tags to newly created or modified notes. With local processing, it ensures privacy and speed while maintaining a seamless tagging workflow.

The Ollama plugin integrates the Ollama AI tool into Obsidian, enabling users to enhance their notes with advanced text manipulation features. Pre-configured prompts such as summarization, explanation, expansion, and various rewriting styles (formal, casual, active voice, bullet points) streamline text editing and content generation. Users can also create custom prompts with specific models and temperature settings. The plugin processes selected text or entire notes and inserts the results back into the document, enhancing productivity and creativity.

The Vector Search plugin enhances Obsidian with semantic search capabilities using Ollama's embedding API. Unlike traditional keyword-based search, it finds notes with similar meanings by generating vector embeddings for markdown content. The plugin enables users to search for related notes based on selected text, configure similarity thresholds, and process files efficiently with automatic indexing and updates. It includes smart text chunking strategies to optimize search results and supports real-time monitoring of file changes.