AI Features
Semantic search, AI chat, and document embeddings powered by Vertex AI and pgvector.
Chat with your documents
Ask questions across your entire document collection. Vertex AI integration provides context-aware answers grounded in your actual content. Chat sessions maintain context for follow-up questions, making it easy to explore complex topics across multiple documents.
- Q1 Planning: Team alignment on roadmap priorities
- Feature Launch: New collaboration tools in beta
- Action Items: 3 follow-ups assigned
Semantic search with vector embeddings
Go beyond keyword matching. Document embeddings (768-dimensional vectors) enable meaning-based search using cosine similarity. Find related content even when the exact words differ. Powered by pgvector with dedicated BullMQ workers for background embedding generation.
Intelligent context building
The AI system automatically builds context from relevant documents, frontmatter, and tags. It understands your document structure and can reference specific sections, making answers more accurate and useful than generic chat interfaces.
- Q1 Planning: Team alignment on roadmap priorities
- Feature Launch: New collaboration tools in beta
- Action Items: 3 follow-ups assigned
Summarize and classify documents
Automatically generate summaries, extract topics, and classify documents into categories. AI analyzes content to suggest relevant tags and surface connections between related notes — helping you maintain an organized, discoverable knowledge base as it grows.
- Create and organize documents
- Use [[wikilinks]] to connect ideas
- Add tags with #hashtags
- Search across all your notes
AI-powered productivity
Discover hidden connections between documents, find relevant content faster, and extract insights from large collections. Multi-turn conversations maintain context across questions, so you can drill deeper into complex topics spanning dozens of documents.
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