Trip planner
Greg Pomerantz
d192b1dcab
The embedding approach was overkill: it embedded only 'name — kind' (short strings), spent a 4B model + ~2.5GB VRAM + 4.6GB of vectors + a one-time 450k-row job to do what indexed SQL does directly. New default — no embedding model, no extra VRAM: - The LLM agent is the semantic layer: it maps the user's concept to OSM kinds + local-language name keywords. It is already in VRAM for chat, so this costs nothing. - /kinds: returns the tag vocabulary that actually exists (GROUP BY kind with counts) so the agent grounds its choices in real data. - /search: indexed retrieval — kind IN/ILIKE (poi_kind), name FTS (to_tsvector) + trigram (pg_trgm) for fuzzy/substring, optional ST_DWithin radius. Ranked by trigram similarity then distance. - schema.sql: real trigram GIN index (poi_name_trgm_ops); renamed the misnamed FTS index to poi_name_fts. - Agent tools: poi_semantic -> poi_kinds + poi_search (both pin results on the map). pgvector demoted to an opt-in path (embed/ + /semantic) — still works if poi_vec is built, but no longer the default. Dropped the half-built poi_vec and stopped the background embed run. |
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| mock | ||
| router | ||
| spatial | ||
| .gitignore | ||
| DESIGN.md | ||
| SURVEY.md | ||