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Author: abrookins

redisvl/extensions/llmcache/semantic.py

@@ -22,7 +22,12 @@
 from redisvl.index import AsyncSearchIndex, SearchIndex
 from redisvl.query import RangeQuery
 from redisvl.query.filter import FilterExpression
-from redisvl.utils.utils import current_timestamp, deprecated_argument, serialize, validate_vector_dims
+from redisvl.utils.utils import (
+    current_timestamp,
+    deprecated_argument,
+    serialize,
+    validate_vector_dims,
+)
 from redisvl.utils.vectorize import BaseVectorizer, HFTextVectorizer
 
 

redisvl/utils/utils.py

@@ -1,6 +1,6 @@
-from functools import wraps
 import json
 from enum import Enum
+from functools import wraps
 from time import time
 from typing import Any, Callable, Dict, Optional
 from uuid import uuid4

tests/integration/test_llmcache.py

@@ -885,6 +885,7 @@ def test_bad_dtype_connecting_to_existing_cache(redis_url):
             name="float64_cache", dtype="float16", redis_url=redis_url
         )
 
+
 def test_vectorizer_dtype_mismatch():
     with pytest.raises(ValueError):
         SemanticCache(

tests/unit/test_base_vectorizer.py

@@ -1,28 +1,29 @@
 from typing import List
-from redisvl.utils.vectorize.base import BaseVectorizer
 
+from redisvl.utils.vectorize.base import BaseVectorizer
 
 
 def test_base_vectorizer_defaults():
     """
-    Test that the base vectorizer defaults are set correctly, with 
+    Test that the base vectorizer defaults are set correctly, with
     a default for dtype. Versions before 0.3.8 did not have this field.
 
     A regression test for langchain-redis/#48
     """
+
     class SimpleVectorizer(BaseVectorizer):
         model: str = "simple"
         dims: int = 10
-        
+
         def embed(self, text: str, **kwargs) -> List[float]:
             return [0.0] * self.dims
-        
+
         async def aembed(self, text: str, **kwargs) -> List[float]:
             return [0.0] * self.dims
-        
+
         async def aembed_many(self, texts: List[str], **kwargs) -> List[List[float]]:
             return [[0.0] * self.dims] * len(texts)
-        
+
         def embed_many(self, texts: List[str], **kwargs) -> List[List[float]]:
             return [[0.0] * self.dims] * len(texts)