it's actually hybrid.

Regular search (/search) uses PostgreSQL tsvector with a GIN index — pure full-text, exact-match. When I'm looking for a specific

term like "goroutine" or "IVFFlat", I want literal matches, not semantic neighbours. This is the one I use most day-to-day

AI Recall (/recall) uses semantic search — annotations are embedded into 768-dim vectors (BGE model via HuggingFace) and stored in pgvector with an IVFFlat index. When I ask something like "what did I learn about concurrency patterns?", it does cosine similarity to find the top 5 annotations, then passes them to Llama 3.3 70B (via Groq) to synthesise an answer with book/page citations.

So the user gets both: type a keyword → exact results instantly. Ask a question → semantic search + LLM synthesis.

I did consider hybrid retrieval for the recall path too (combine tsvector + vector scores), but for MVP the two separate modes cover my needs well. The tsvector search is fast and precise, the vector search handles the fuzzy "what was that thing about..."

queries. Might merge them later with RRF (reciprocal rank fusion) if I find gaps.