fix(evidence): fail closed when BM25 is unavailable

harness/tht/adapters/vector/qdrant.py

@@ -102,6 +102,7 @@ class QdrantVectorStore:
                 write_reachable=False,
                 write_detail=str(exc),
                 expected_dimension=self._expected_dimension,
+                bm25_compatible=None,
             )
 
         dimension = info["config"]["params"]["vectors"]["size"]
@@ -118,6 +119,7 @@ class QdrantVectorStore:
             expected_dimension=self._expected_dimension,
             observed_dimensions=dimensions,
             dimension_compatible=compatible,
+            bm25_compatible=self._bm25_compatible(info),
         )
 
     def search(
@@ -176,6 +178,7 @@ class QdrantVectorStore:
                 raise VectorStoreError("Hybrid BM25 is only available for Evidence")
             if query_text.strip() == "" or query_language not in _BM25_LANGUAGES:
                 raise VectorStoreError("Evidence BM25 query is invalid")
+            self._ensure_collection(strict=False, require_bm25=True)
             shared_filter = {"must": filter_must}
             response = self._call(
                 "POST",
@@ -225,7 +228,10 @@ class QdrantVectorStore:
 
     def upsert(self, collection: str, records: list[VectorWriteRecord]) -> int:
         validate_collection(collection)
-        self._ensure_collection(strict=True)
+        self._ensure_collection(
+            strict=True,
+            require_bm25=any(record.sparse_text is not None for record in records),
+        )
         points = []
         for write_record in records:
             validate_collection_kinds(collection, [write_record.record.kind])
@@ -377,7 +383,15 @@ class QdrantVectorStore:
                 else "Embedding dimension does not match configured dimension"
             )
 
-    def _ensure_collection(self, *, strict: bool) -> dict | None:
+    @staticmethod
+    def _bm25_compatible(info: dict) -> bool:
+        sparse_vectors = info.get("config", {}).get("params", {}).get("sparse_vectors")
+        if not isinstance(sparse_vectors, dict):
+            return False
+        bm25 = sparse_vectors.get("bm25")
+        return isinstance(bm25, dict) and bm25.get("modifier") == "idf"
+
+    def _ensure_collection(self, *, strict: bool, require_bm25: bool = False) -> dict | None:
         response = self._call("GET", f"/collections/{self._collection}", None, allow_missing=True)
         if response is None:
             if not strict:
@@ -420,6 +434,8 @@ class QdrantVectorStore:
                     f"/collections/{self._collection}/index",
                     {"field_name": field_name, "field_schema": "keyword"},
                 )
+        if require_bm25 and not self._bm25_compatible(result):
+            raise VectorStoreError("Evidence BM25 collection configuration mismatch")
         return result
 
     def _scroll(self, must: list[dict]) -> list[dict]: