# Model Corpus via model-scoped collections

Vectors for Cloud and Local Embedding Models must not share a Qdrant collection: dimensions and embedding spaces differ. Each Embedding Model therefore owns a **Model Corpus** — Strategy collections whose identity includes that model. Process, query, and Experiment only read/write the Active Embedding Model’s corpus; other corpora stay untouched. Pre-existing unscoped collections (e.g. `fixed_size_collection`) are migrated into the default cloud Embedding Model’s corpus so existing OpenAI work is not discarded.

## Considered Options

- **Model-scoped collections** — chosen; non-destructive switch between Providers/models; clear isolation
- **Wipe-and-rebuild on switch** — simple naming, destructive and easy to forget
- **Dimension-gated single set** — only allow same-dimension swaps; blocks most OpenAI ↔ Ollama use

Legacy handling (original): **migrate unscoped → default cloud corpus**. **Superseded for naming by ADR-0021** — all models, including default cloud, use `{strategy}__{model_id}_collection`; delete leftover unscoped collections when rebuilding.

## Consequences

- Collection naming and vector size are driven by the Embedding Model Registry entry, not a global `1536` constant
- Admin Qdrant list shows all corpora, labeled by Embedding Model, with the active corpus emphasized; wipe stays per named collection
- Switching Active Embedding Model does not delete the other Model Corpus; operator must process documents again under the new model to populate it