slm-recall

To recall past project decisions and notes efficiently, you can combine semantic similarity, knowledge graph traversal, and full-text search to retrieve relevant memories with ranked relevance across projects and timelines.

The best way to search local memories is using a multi-method approach that combines semantic similarity for conceptual matches, knowledge graph traversal for entity connections, and full-text search to retrieve offline local data.

Yes, local memory search allows you to locate past conversations and reconstruct API design decision rationale by applying semantic similarity and knowledge graph traversal to find related memories and notes.

Offline local memory processing does not require external dependencies, allowing you to rank search results by relevance and recency entirely through local semantic search and knowledge graph traversal.

Knowledge graph traversal exposes connections between stored memories, decisions, and entities by mapping relationships across the local knowledge base, allowing multi-method search to surface conceptually related items quickly.

You should avoid using full-text search alone when conceptual relationships matter; combining it with semantic similarity and knowledge graph traversal provides a more accurate, relevance-ranked recall of past decisions and notes.