feat(embedding): expose model_version on dense and sparse embedder ports

Why:
- ADR-0001's Qdrant payload records embedding_model_version so a future model
  swap can identify which chunks need re-embedding. The embedder is what
  knows which model produced its vectors, so it reports this rather than the
  call site reconstructing it from settings.

Changes:
- DenseEmbedder/SparseEmbedder protocols gain a model_version: str attribute.
- OpenAICompatibleEmbedder reports its configured model; Bm25SparseEmbedder
  reports its analyzer (bm25-<analyzer>).

src/infrastructure/embedding/bm25.py

@@ -86,6 +86,7 @@ class Bm25SparseEmbedder:
     name = "sparse"
 
     def __init__(self, settings: SparseEmbeddingSettings) -> None:
+        self.model_version = f"bm25-{settings.analyzer}"
         self._settings = settings
 
     def embed_batch(self, texts: Sequence[str], *, query: bool = False) -> list[SparseVector]:

src/infrastructure/embedding/openai_compatible.py

@@ -52,6 +52,7 @@ class OpenAICompatibleEmbedder:
         keep_alive: str | None = None,
     ) -> None:
         self.name = name
+        self.model_version = model
         self._client = client
         self._model = model
         self._dimensions = dimensions