For AI agents: a documentation index is available at the root level at /llms.txt and /llms-full.txt. Append /llms.txt to any URL for a page-level index, or .md for the markdown version of any page.
Compass is a search engine that runs as a single binary on your hardware. Two things make it different from a typical vector database:
Run multiple embedding models on the same collection. A text model for transcripts, a multimodal model for video frames. Search one space, or merge results across all of them in one query.
Upgrade embedding models without downtime. Compass re-embeds in the background and atomically swaps the default when the new space is ready. The collection keeps serving queries the whole time.
In addition to vector search, it also combines BM25 full-text search (via Tantivy) with HNSW vector search (via USearch), merges them with Reciprocal Rank Fusion.
Other notable features
Built-in embedding inference (BGE-small bundled, point at a GPU endpoint for larger models)
Video-specific querying (BBC TAMS support)
Custom metadata sidecar support
Recency bias and highly customizable metadata filtering
You want full control over your embedding setup: model change management, evaluation pipelines, and hybrid search tuning.
You’re building an agent that retrieves from a mixed collection of video transcripts, sidecar metadata, visual frames, and text documents, and you need one query to search all of it.
Your data has to stay in your VPC. You can’t send content to a third-party API at query time.
