Persistent RAG (Retrieval-Augmented Generation)
Memo isn't just a chat; it's a "Second Brain."
- Semantic Indexing: Every interaction is automatically embedded and stored in a local vector database.
- Hybrid Search: Retrieval combines vector similarity with FTS5 keyword search (merged via Reciprocal Rank Fusion), so a short, exact fact isn't only found by "close enough" semantic distance.
- Compound-Question Splitting: A multi-topic question ("what's my name, birthday, and favorite color") is split on conjunctions so each topic gets its own search instead of being diluted into one blended embedding.
- Contextual Recall: Before every response, Memo performs this hybrid search to retrieve the most relevant past conversations (Top-K matching).
- Pinned Facts (2026-07-15): Durable personal facts (name, birthday, pets, etc.) — whether saved via
/rememberor automatically detected from ordinary conversation — are injected into every prompt unconditionally, bypassing retrieval ranking entirely, so they're never crowded out by routine chat. - Infinite Context: Long-term memory allows the AI to remember details from weeks or months ago, regardless of the current model's window.