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.

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

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")