Update #704 to not require a path parameter in Embeddings as not all …

…vector models need a path. Update test.

src/python/txtai/embeddings/base.py

@@ -744,7 +744,7 @@ def configure(self, config):
         self.scoring = self.createscoring() if scoring and (not isinstance(scoring, dict) or not scoring.get("terms")) else None
 
         # Dense vectors - transforms data to embeddings vectors
-        self.model = self.loadvectors() if self.config and self.config.get("path") else None
+        self.model = self.loadvectors() if self.config else None
 
         # Query model
         self.query = self.loadquery() if self.config else None

test/python/testoptional.py

@@ -253,7 +253,7 @@ def testVectors(self):
             VectorsFactory.create({"method": "words"}, None)
 
         with self.assertRaises(ImportError):
-            VectorsFactory.create({"method": "sentence-transformers", "path": ""}, None)
+            VectorsFactory.create({"method": "sentence-transformers", "path": "sentence-transformers/nli-mpnet-base-v2"}, None)
 
     def testWorkflow(self):
         """