ssot-search

To search an Obsidian knowledge base for past architecture decisions, this Skill uses full-text retrieval combined with semantic reranking to locate and return the most relevant stored technical notes and historical context.

You can use semantic search to find configuration records because the Skill applies sentence-transformers based retrieval to rank and extract specific technical details from stored documents.

The best way to retrieve historical context from an SSOT knowledge base is using a search workflow that applies ripgrep for full-text matching and semantic ranking to surface relevant past decisions.

Yes, performing semantic search on a knowledge base requires SSOT search tooling with ripgrep for full-text retrieval and sentence-transformers to process and rank the semantic relevance of stored documents.

Semantic ranking improves knowledge base search results by evaluating the contextual meaning of technical notes with sentence-transformers, ensuring that retrieved architecture decisions and configuration details match user intent rather than just keyword frequency.

You can retrieve a previous GLM proxy configuration decision by querying the knowledge base, which uses full-text search and semantic reranking to instantly find and return the specific historical reference.