Rocketnotes

Rocketnotes is a web-based Markdown note taking app with LLM-powered text completion, chat and semantic search. It utilizes a 100% serverless RAG pipeline build with langchain, sentence-transformers, faiss, Ollama and OpenAI or Anthropic API.
Checkout how the serverless RAG pipeline works here Serverless RAG on AWS.

How to run

• You can # for free
• Run it 100% locally with Docker
• Deploy to AWS

✨ Features

• 📝 Code syntax highlighting
• 🌳 Hierarchical document tree with draggable nodes
• 🌐 Document sharing
• 🔍 Content search
• 🔦 Semantic search
• ✍️ Copilot-like text completion
• 🤖 Chat with your documents
  • Servlerless RAG with faiss, OpenAI and/or Anthropic
• 📦 Local mode with Docker
  • use Ollama and/or Sentence Transformers for 100% local RAG
• 📥 Zettelkasten with semantic archiving
  • Use vector index to insert notes into highest semantic-ranked documents

 

Chat with your documents or do semantic search

• 🤖 Use the power of LLMs together with vector embeddings to chat with your notes or search them semantically.

LLM-powered text completion

• 🤖 Get Copilot-like text completion autosuggestions.

Zettelkasten with semantic archiving

• ✍️ Save your daily note snippets into zettelkasten.
• 📥 Use vector index to insert notes into highest semantic-ranked documents with ease.

 

Create code snippets with syntax highlighting

• 📝 Use the power and simplicity of Markdown for your personal notes.
• 💻 Create useful code snippets in your favourite programming language with syntax highlighting.
• 📖 Share documents with external users.

 

Superfast Document Search

• 🔎 Search through all your documents by content.
• 🚀 Get an autosuggestion panel with all documents matching you search pattern - superfast!

 

Hierarchical Document Tree

• 📚 Save your note documents hierarchical with unlimited depth of subdocuments.
• 🗂️ Structure your notes by simply drag and drop the desired document.
• 🌟 Pin favourite documents for fast top-level access.

 

🌟 Contributing

The most comfortable way to get started is to open the project in a ready-to-code Gitpod workspace with all packages & tools preinstalled and a running database with sample data.

If you prefer to setup the project on your local machine visit the get started guide with all required steps to run the project locally in development mode.

You find also the contribution guidelines there. Don't hesitate to open an issue for getting some feedback about a potential bug or if you desire a missing feature. We also appreciate to check over current issues and provide feedback to existing ones or even raise a PR which solves an issue. Any contribution is welcome!