Obsidian plugins with #semantic-search#

The Zettelkasten LLM Tools plugin enhances note-taking in Obsidian by leveraging Large Language Models for advanced features like semantic search and automatic note improvement. The plugin allows users to generate embeddings for their notes, making it easy to find similar notes through semantic search. It also offers a copilot feature to help rewrite and improve notes by suggesting clearer titles, focused ideas, and relevant links. Additionally, users can batch generate embeddings for multiple notes based on customizable filename patterns. This tool helps streamline the Zettelkasten method by organizing and improving note connections using AI-powered insights.

The Similar Notes plugin finds and recommends notes that are semantically related to what you're currently writing, using language models for real-time analysis. It displays up to five similar notes at the bottom of your note, making it easier to spot hidden connections in your ideas without relying on exact keyword matches. The plugin is self-contained with its own vector database and gives you the choice between built in Hugging Face models or connecting to Ollama for custom embeddings. No external cloud API is needed, and it smartly ignores notes already linked from your current document.

The Vault Chat plugin integrates OpenAI's ChatGPT into Obsidian, enabling users to interact with an AI assistant trained on their vault. Key features include the ability to chat with ChatGPT about your entire vault or the active note, summarize notes, and perform semantic searches. This enhances productivity and understanding by providing contextual insights and easy access to information stored in your vault. The plugin requires indexing of your vault and uses an OpenAI API key for operation, offering an intuitive way to manage and engage with your notes.

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.