Changes needed to work with jina 2.0.13 (python 3.9)

wikipedia-sentences/app.py

@@ -21,7 +21,7 @@ def print_topk(resp, sentence):
         print(f"\n\n\nTa-Dah🔮, here's what we found for: {sentence}")
         for idx, match in enumerate(doc.matches):
 
-            score = match.score.value
+            score = match.scores['cosine'].value
             if score < 0.0:
                 continue
             print(f'> {idx:>2d}({score:.2f}). {match.text}')

wikipedia-sentences/indexer.py

@@ -3,6 +3,7 @@
 
 from jina import Document, requests, Executor, DocumentArray
 
+
 class NumpyIndexer(Executor):
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
@@ -12,7 +13,6 @@ def __init__(self, **kwargs):
     def index(self, docs: 'DocumentArray', **kwargs):
         self._docs.extend(docs)
 
-
     @requests(on='/search')
     def search(self, docs: 'DocumentArray', parameters: Dict = None, **kwargs):
         if parameters is None:
@@ -26,12 +26,12 @@ def search(self, docs: 'DocumentArray', parameters: Dict = None, **kwargs):
         for _q, _positions, _dists in zip(docs, positions, dist):
             for position, _dist in zip(_positions, _dists):
                 d = Document(self._docs[int(position)])
-                d.score.value = 1 - _dist
+                d.scores['cosine'] = 1 - _dist
                 _q.matches.append(d)
 
     @staticmethod
     def _get_sorted_top_k(
-        dist: 'np.array', top_k: int
+            dist: 'np.array', top_k: int
     ) -> Tuple['np.ndarray', 'np.ndarray']:
         if top_k >= dist.shape[1]:
             idx = dist.argsort(axis=1)[:, :top_k]
@@ -44,31 +44,37 @@ def _get_sorted_top_k(
             dist = np.take_along_axis(dist, idx_fs, axis=1)
 
         return idx, dist
-
+
+
 def _get_ones(x, y):
     return np.ones((x, y))
 
+
 def _ext_A(A):
     nA, dim = A.shape
     A_ext = _get_ones(nA, dim * 3)
-    A_ext[:, dim : 2 * dim] = A
-    A_ext[:, 2 * dim :] = A ** 2
+    A_ext[:, dim: 2 * dim] = A
+    A_ext[:, 2 * dim:] = A ** 2
     return A_ext
 
+
 def _ext_B(B):
     nB, dim = B.shape
     B_ext = _get_ones(dim * 3, nB)
     B_ext[:dim] = (B ** 2).T
-    B_ext[dim : 2 * dim] = -2.0 * B.T
+    B_ext[dim: 2 * dim] = -2.0 * B.T
     del B
     return B_ext
 
+
 def _euclidean(A_ext, B_ext):
     sqdist = A_ext.dot(B_ext).clip(min=0)
     return np.sqrt(sqdist)
 
+
 def _norm(A):
     return A / np.linalg.norm(A, ord=2, axis=1, keepdims=True)
 
+
 def _cosine(A_norm_ext, B_norm_ext):
     return A_norm_ext.dot(B_norm_ext).clip(min=0) / 2

wikipedia-sentences/requirements.txt

@@ -1,4 +1,4 @@
-click==7.1.2
+click==8.0.1
 transformers==4.6.1
 torch==1.7.1
-jina[scipy, http]==2.0.0rc5.dev15
+jina[scipy, http]==2.0.13