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/application/ports/embedding.py

@@ -19,6 +19,14 @@ class DenseEmbedder(Protocol):
     """
 
     name: str
+    model_version: str
+    """Identifies the model that produced these vectors (ADR-0001).
+
+    Written into every point's `embedding_model_version` payload field, which
+    exists so a future model swap can tell which chunks need re-embedding. The
+    embedder is what knows this, so it is reported here rather than
+    reconstructed from configuration at the call site.
+    """
 
     async def embed_batch(self, texts: Sequence[str]) -> list[list[float]]:
         """Return one vector per input text, same order. Raises `EmbedderError`
@@ -37,6 +45,12 @@ class SparseEmbedder(Protocol):
     """
 
     name: str
+    model_version: str
+    """Identifies the analyzer/parameters that produced these vectors.
+
+    Same purpose as `DenseEmbedder.model_version`; for BM25 the "model" is the
+    analyzer choice (ADR-0005), which is equally a re-embedding trigger.
+    """
 
     def embed_batch(self, texts: Sequence[str], *, query: bool = False) -> list[SparseVector]:
         """Return one sparse vector per input text, same order.