Format code with ruff

apps/history_data/wechat_history.py

@@ -314,7 +314,9 @@ class WeChatHistoryReader(BaseReader):
 
         return concatenated_groups
 
-    def _create_concatenated_content(self, message_group: dict, contact_name: str) -> tuple[str, str]:
+    def _create_concatenated_content(
+        self, message_group: dict, contact_name: str
+    ) -> tuple[str, str]:
         """
         Create concatenated content from a group of messages.
 

apps/multimodal/vision-based-pdf-multi-vector/vidore_v2_benchmark.py

@@ -113,7 +113,8 @@ def load_vidore_v2_data(
                 # Try to get a sample to see actual language values
                 try:
                     sample_ds = cast(
-                        Dataset, load_dataset(dataset_path, "queries", split=split, revision=revision)
+                        Dataset,
+                        load_dataset(dataset_path, "queries", split=split, revision=revision),
                     )
                     if len(sample_ds) > 0 and "language" in sample_ds.column_names:
                         sample_langs = set(sample_ds["language"])

tests/test_prompt_template_persistence.py

@@ -581,7 +581,18 @@ class TestQueryTemplateApplicationInComputeEmbedding:
 
         # Create a concrete implementation for testing
         class TestSearcher(BaseSearcher):
-            def search(self, query, top_k, complexity=64, beam_width=1, prune_ratio=0.0, recompute_embeddings=False, pruning_strategy="global", zmq_port=None, **kwargs):
+            def search(
+                self,
+                query,
+                top_k,
+                complexity=64,
+                beam_width=1,
+                prune_ratio=0.0,
+                recompute_embeddings=False,
+                pruning_strategy="global",
+                zmq_port=None,
+                **kwargs,
+            ):
                 return {"labels": [], "distances": []}
 
         searcher = object.__new__(TestSearcher)
@@ -625,7 +636,18 @@ class TestQueryTemplateApplicationInComputeEmbedding:
 
         # Create a concrete implementation for testing
         class TestSearcher(BaseSearcher):
-            def search(self, query, top_k, complexity=64, beam_width=1, prune_ratio=0.0, recompute_embeddings=False, pruning_strategy="global", zmq_port=None, **kwargs):
+            def search(
+                self,
+                query,
+                top_k,
+                complexity=64,
+                beam_width=1,
+                prune_ratio=0.0,
+                recompute_embeddings=False,
+                pruning_strategy="global",
+                zmq_port=None,
+                **kwargs,
+            ):
                 return {"labels": [], "distances": []}
 
         searcher = object.__new__(TestSearcher)
@@ -671,7 +693,18 @@ class TestQueryTemplateApplicationInComputeEmbedding:
         from leann.searcher_base import BaseSearcher
 
         class TestSearcher(BaseSearcher):
-            def search(self, query, top_k, complexity=64, beam_width=1, prune_ratio=0.0, recompute_embeddings=False, pruning_strategy="global", zmq_port=None, **kwargs):
+            def search(
+                self,
+                query,
+                top_k,
+                complexity=64,
+                beam_width=1,
+                prune_ratio=0.0,
+                recompute_embeddings=False,
+                pruning_strategy="global",
+                zmq_port=None,
+                **kwargs,
+            ):
                 return {"labels": [], "distances": []}
 
         searcher = object.__new__(TestSearcher)
@@ -710,7 +743,18 @@ class TestQueryTemplateApplicationInComputeEmbedding:
         from leann.searcher_base import BaseSearcher
 
         class TestSearcher(BaseSearcher):
-            def search(self, query, top_k, complexity=64, beam_width=1, prune_ratio=0.0, recompute_embeddings=False, pruning_strategy="global", zmq_port=None, **kwargs):
+            def search(
+                self,
+                query,
+                top_k,
+                complexity=64,
+                beam_width=1,
+                prune_ratio=0.0,
+                recompute_embeddings=False,
+                pruning_strategy="global",
+                zmq_port=None,
+                **kwargs,
+            ):
                 return {"labels": [], "distances": []}
 
         searcher = object.__new__(TestSearcher)
@@ -774,7 +818,18 @@ class TestQueryTemplateApplicationInComputeEmbedding:
         from leann.searcher_base import BaseSearcher
 
         class TestSearcher(BaseSearcher):
-            def search(self, query, top_k, complexity=64, beam_width=1, prune_ratio=0.0, recompute_embeddings=False, pruning_strategy="global", zmq_port=None, **kwargs):
+            def search(
+                self,
+                query,
+                top_k,
+                complexity=64,
+                beam_width=1,
+                prune_ratio=0.0,
+                recompute_embeddings=False,
+                pruning_strategy="global",
+                zmq_port=None,
+                **kwargs,
+            ):
                 return {"labels": [], "distances": []}
 
         searcher = object.__new__(TestSearcher)

tests/test_readme_examples.py

@@ -98,7 +98,9 @@ def test_backend_options():
     with tempfile.TemporaryDirectory() as temp_dir:
         # Use smaller model in CI to avoid memory issues
         is_ci = os.environ.get("CI") == "true"
-        embedding_model = "sentence-transformers/all-MiniLM-L6-v2" if is_ci else "facebook/contriever"
+        embedding_model = (
+            "sentence-transformers/all-MiniLM-L6-v2" if is_ci else "facebook/contriever"
+        )
         dimensions = 384 if is_ci else None
 
         # Test HNSW backend (as shown in README)