Jarvis is an AI-powered assistant plugin for the Joplin note-taking application, designed to enhance productivity through advanced natural language processing. It caters to users who want to leverage AI for tasks like content generation, summarization, semantic search, and automated literature reviews directly within their notes, supporting both online and offline LLM backends.

How It Works

Jarvis integrates with Joplin by providing several commands accessible through the application's interface. It utilizes various LLMs (GPT, Claude, Gemini, Ollama, Hugging Face) for different functionalities. For instance, "Chat with your notes" sends relevant note excerpts along with the prompt to the LLM for context-aware conversations. Semantic search employs local or online models to find notes based on similarity. Literature reviews query academic databases like Semantic Scholar and Crossref, compiling findings into structured reports.

Quick Start & Requirements

• Install from Joplin's plugin marketplace or download from GitHub.
• Requires Joplin.
• Configuration involves setting API keys for chosen LLM services (OpenAI, Google AI, Hugging Face, etc.) and potentially literature search engines (Scopus, Elsevier, Springer).
• Increasing "Memory tokens" in settings can improve chat coherence.

Highlighted Details

• Supports a wide range of LLMs, including local options via Ollama and Hugging Face.
• Features semantic search that can operate locally without sending note content to remote servers.
• Automated literature review functionality integrates with multiple academic databases.
• Offers note annotation (title, summary, links, tags) and text autocompletion.

Maintenance & Community

• Community discussion available on the Joplin discourse forum: https://discourse.joplinapp.org/t/28316.

Licensing & Compatibility

• The README does not explicitly state a license. Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

• The plugin sends user prompts and note content to selected LLM services, which may incur charges from those services. API keys should be managed carefully.
• The developer is not affiliated with the LLM or search services used.