Fix migration workflow: upload as artifact instead of scp to practice EC2

apps/ai-service/adapters/qdrant.py

@@ -15,6 +15,45 @@ _LEXICAL_STOPWORDS = frozenset({
 })
 
 
+def _vector_search_points(
+    client: Any,
+    *,
+    collection_name: str,
+    vector: list[float],
+    query_filter: Any,
+    limit: int,
+) -> list[Any]:
+    """Run a dense lookup across supported qdrant-client generations.
+
+    qdrant-client 1.16 removed ``QdrantClient.search`` in favour of the
+    universal ``query_points`` API.  Developer machines can still have an
+    older 1.x client because the project allows ``>=1.7,<2``.  Prefer the new
+    API when present and retain the old call only as a compatibility path;
+    both return scored points with payloads.
+    """
+    query_points = getattr(client, "query_points", None)
+    if callable(query_points):
+        response = query_points(
+            collection_name=collection_name,
+            query=vector,
+            query_filter=query_filter,
+            limit=limit,
+            with_payload=True,
+        )
+        return list(response.points)
+
+    search = getattr(client, "search", None)
+    if callable(search):
+        return list(search(
+            collection_name=collection_name,
+            query_vector=vector,
+            query_filter=query_filter,
+            limit=limit,
+            with_payload=True,
+        ))
+    raise RuntimeError("qdrant client exposes neither query_points nor search")
+
+
 class QueryEmbedder(Protocol):
     @property
     def dimensions(self) -> int: ...
@@ -133,14 +172,14 @@ class QdrantRetriever:
                 f"query vector has {len(vector)} dimensions; "
                 f"expected {self._embedder.dimensions}"
             )
-        points = self._client.search(
+        points = _vector_search_points(
+            self._client,
             collection_name=self._collection_name,
-            query_vector=vector,
+            vector=vector,
             query_filter=Filter(
                 must=[FieldCondition(key="drug_id", match=MatchValue(value=drug_id))]
             ),
             limit=limit,
-            with_payload=True,
         )
         return [
             SearchHit(_document(dict(point.payload or {})), float(point.score))
@@ -410,9 +449,10 @@ class QdrantRetriever:
                 f"query vector has {len(vector)} dimensions; "
                 f"expected {self._embedder.dimensions}"
             )
-        points = self._client.search(
+        points = _vector_search_points(
+            self._client,
             collection_name=self._collection_name,
-            query_vector=vector,
+            vector=vector,
             query_filter=Filter(
                 must=[
                     FieldCondition(key="section_key", match=MatchValue(value="chi_dinh")),
@@ -420,7 +460,6 @@ class QdrantRetriever:
                 ]
             ),
             limit=limit * 4,
-            with_payload=True,
         )
         hits: list[SearchHit] = []
         for point in points: