The ollama recipe declared a single `default_dims: 768` (nomic-embed-text's
width) while serving models spanning 384..4096. Every non-nomic model
resolved to 768, so `gbrain init --embedding-model ollama:bge-m3` built a
768-wide `content_chunks.embedding` column for a model that emits 1024. The
schema looked fine and only failed at first insert with
`expected 768 dimensions, not 1024`.
Adds an optional `model_dims` map to `EmbeddingTouchpoint` and an
`embeddingDimsForModel()` resolver that prefers the per-model entry and falls
back to `default_dims`. The ollama recipe declares real widths for the models
it lists; bge-m3 is added to that list. The three `init` call sites that read
`default_dims` now resolve per model.
Partial by design: unlisted models still fall back to `default_dims`, and
`trust_custom_dims` keeps an explicit `--embedding-dimensions` override
working. `user_provided_models` recipes (litellm, llama-server) still resolve
to 0, so they continue to require explicit dimensions.
Verified end to end against an OpenAI-compatible stub standing in for Ollama,
using an isolated GBRAIN_HOME:
before: config 768, content_chunks.embedding vector(768), insert fails
after: config 1024, content_chunks.embedding vector(1024), insert succeeds
