fix(indexer): fix vec np.concat

gnes/indexer/base.py

@@ -51,7 +51,7 @@ def query(self, keys: np.ndarray, top_k: int, *args, **kwargs) -> List[List[Tupl
     def query_and_score(self, q_chunks: List['gnes_pb2.Chunk'], top_k: int, *args, **kwargs) -> List[
         'gnes_pb2.Response.QueryResponse.ScoredResult']:
         vecs = [blob2array(c.embedding) for c in q_chunks]
-        queried_results = self.query(np.concatenate(vecs, 0), top_k=top_k)
+        queried_results = self.query(np.stack(vecs), top_k=top_k)
         results = []
         for q_chunk, topk_chunks in zip(q_chunks, queried_results):
             for _doc_id, _offset, _weight, _relevance in topk_chunks:

gnes/service/indexer.py

@@ -48,17 +48,15 @@ def _handler_chunk_index(self, msg: 'gnes_pb2.Message'):
                 self.logger.warning('document (doc_id=%s) contains no chunks!' % d.doc_id)
                 continue
 
-            for c in d.chunks:
-                self.logger.info(c.embedding)
             vecs += [blob2array(c.embedding) for c in d.chunks]
             doc_ids += [d.doc_id] * len(d.chunks)
             offsets += [c.offset for c in d.chunks]
             weights += [c.weight for c in d.chunks]
 
-            self.logger.info('%d %d %d %d' % (len(vecs), len(doc_ids), len(offsets), len(weights)))
-            self.logger.info(np.concatenate(vecs, 0).shape)
+            # self.logger.info('%d %d %d %d' % (len(vecs), len(doc_ids), len(offsets), len(weights)))
+            # self.logger.info(np.stack(vecs).shape)
         if vecs:
-            self._model.add(list(zip(doc_ids, offsets)), np.concatenate(vecs, 0), weights)
+            self._model.add(list(zip(doc_ids, offsets)), np.stack(vecs), weights)
 
     def _handler_doc_index(self, msg: 'gnes_pb2.Message'):
         self._model.add([d.doc_id for d in msg.request.index.docs],