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>).

tests/fakes.py

@@ -29,6 +29,7 @@ class FakeDenseEmbedder:
 
     name: str
     dimensions: int = 4
+    model_version: str = "fake-dense-v1"
     calls: list[list[str]] = field(default_factory=list)
     fail_next: bool = False
     delay_seconds: float = 0.0
@@ -49,6 +50,7 @@ class FakeSparseEmbedder:
     """A scripted `SparseEmbedder`. Returns an empty sparse vector per text."""
 
     name: str = "sparse"
+    model_version: str = "fake-sparse-v1"
     calls: list[list[str]] = field(default_factory=list)
     fail_next: bool = False
 
@@ -58,3 +60,4 @@ class FakeSparseEmbedder:
             self.fail_next = False
             raise RuntimeError("simulated embedder failure")
         return [SparseVector(indices=[], values=[]) for _ in texts]
+

tests/unit/application/test_embedding.py

@@ -37,6 +37,7 @@ class _TrackingDenseEmbedder:
     """
 
     name: str
+    model_version: str = "stub-v1"
     dimensions: int = 3
     batches: list[list[str]] = field(default_factory=list)
     in_flight: int = 0
@@ -54,6 +55,7 @@ class _TrackingDenseEmbedder:
 @dataclass
 class _FailingDenseEmbedder:
     name: str
+    model_version: str = "stub-v1"
 
     async def embed_batch(self, texts: Sequence[str]) -> list[list[float]]:
         raise RuntimeError("boom")
@@ -62,6 +64,7 @@ class _FailingDenseEmbedder:
 @dataclass
 class _StubSparseEmbedder:
     name: str = "sparse"
+    model_version: str = "stub-sparse-v1"
     calls: list[list[str]] = field(default_factory=list)
 
     def embed_batch(self, texts: Sequence[str], *, query: bool = False) -> list[SparseVector]:
@@ -72,6 +75,7 @@ class _StubSparseEmbedder:
 @dataclass
 class _FailingSparseEmbedder:
     name: str = "sparse"
+    model_version: str = "stub-sparse-v1"
 
     def embed_batch(self, texts: Sequence[str], *, query: bool = False) -> list[SparseVector]:
         raise RuntimeError("boom")