minne/docs/features.md

Features

Search vs Chat

Search — Use when you know what you're looking for. Full-text search matches query terms across your content.

Chat — Use when exploring concepts or reasoning about your knowledge. The AI analyzes your query and retrieves relevant context from your entire knowledge base.

Content Processing

Minne automatically processes saved content:

1. Web scraping extracts readable text from URLs (via headless Chrome)
2. Text analysis identifies key concepts and relationships
3. Graph creation builds connections between related content
4. Embedding generation enables semantic search

Knowledge Graph

Explore your knowledge as an interactive network:

• Manual curation — Create entities and relationships yourself
• AI automation — Let AI extract entities and discover relationships
• Hybrid approach — AI suggests connections for your approval

The D3-based graph visualization shows entities as nodes and relationships as edges.

Hybrid Retrieval

Minne combines multiple retrieval strategies:

• Vector similarity — Semantic matching via embeddings
• Full-text search — Keyword matching with BM25
• Graph traversal — Following relationships between entities

Results are merged using Reciprocal Rank Fusion (RRF) for optimal relevance.

Reranking (Optional)

When enabled, retrieval results are rescored with a cross-encoder model for improved relevance. Powered by fastembed-rs.

Trade-offs:

• Downloads ~1.1 GB of model data
• Adds latency per query
• Potentially improves answer quality, see blog post

Enable via RERANKING_ENABLED=true. See Configuration.

Multi-Format Ingestion

Supported content types:

• Plain text and notes
• URLs (web pages)
• PDF documents
• Audio files
• Images

Scratchpad

Quickly capture content without committing to permanent storage. Convert to full content when ready.

iOS Shortcut

Use the Minne iOS Shortcut for quick content capture from your phone.