feat(evidence): evaluate retrieval with a small fixture

harness/tht/adapters/vector/qdrant.py

@@ -132,6 +132,7 @@ class QdrantVectorStore:
         metadata_filter: dict[str, object] | None = None,
         query_text: str | None = None,
         query_language: str | None = None,
+        retrieval_mode: str = "fused",
     ) -> list[VectorHit]:
         require_positive_limit(limit)
         self._validate_embedding(embedding, query=True)
@@ -185,7 +186,40 @@ class QdrantVectorStore:
                 if not isinstance(values, list) or not all(isinstance(item, str) for item in values):
                     raise VectorStoreError("Invalid vector metadata filter")
                 filter_must.extend({"key": payload_key, "match": {"value": item}} for item in values)
-        if query_text is None:
+        if retrieval_mode not in {"fused", "dense", "bm25"}:
+            raise VectorStoreError("Evidence retrieval mode is invalid")
+        if retrieval_mode == "dense":
+            if allowed_record_kinds != ["evidence"]:
+                raise VectorStoreError("Evidence branch diagnostics are only available for Evidence")
+            response = self._call(
+                "POST",
+                f"/collections/{self._collection}/points/query",
+                {
+                    "vector": embedding,
+                    "limit": limit,
+                    "with_payload": True,
+                    "filter": {"must": filter_must},
+                },
+            )
+        elif retrieval_mode == "bm25":
+            if allowed_record_kinds != ["evidence"]:
+                raise VectorStoreError("Evidence branch diagnostics are only available for Evidence")
+            if query_text is None or 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",
+                f"/collections/{self._collection}/points/query",
+                {
+                    "query": self._bm25_document(query_text, query_language),
+                    "using": "bm25",
+                    "limit": limit,
+                    "with_payload": True,
+                    "filter": shared_filter,
+                },
+            )
+        elif query_text is None:
             if allowed_record_kinds == ["evidence"]:
                 raise VectorStoreError("Evidence hybrid query text is required")
             response = self._call(