fix formatting

Author: tylerhutcherson

redisvl/utils/vectorize/base.py

@@ -81,7 +81,9 @@ def batchify(self, seq: list, size: int, preprocess: Optional[Callable] = None):
             else:
                 yield seq[pos : pos + size]
 
-    def _process_embedding(self, embedding: List[float], as_buffer: bool, dtype: Optional[str]):
+    def _process_embedding(
+        self, embedding: List[float], as_buffer: bool, dtype: Optional[str]
+    ):
         if as_buffer:
             if not dtype:
                 raise RuntimeError(

redisvl/utils/vectorize/text/azureopenai.py

@@ -189,7 +189,7 @@ def embed_many(
             raise TypeError("Must pass in a list of str values to embed.")
         if len(texts) > 0 and not isinstance(texts[0], str):
             raise TypeError("Must pass in a list of str values to embed.")
-        
+
         dtype = kwargs.pop("dtype", None)
 
         embeddings: List = []
@@ -233,7 +233,7 @@ def embed(
 
         if preprocess:
             text = preprocess(text)
-        
+
         dtype = kwargs.pop("dtype", None)
 
         result = self._client.embeddings.create(input=[text], model=self.model)
@@ -273,7 +273,7 @@ async def aembed_many(
             raise TypeError("Must pass in a list of str values to embed.")
         if len(texts) > 0 and not isinstance(texts[0], str):
             raise TypeError("Must pass in a list of str values to embed.")
-        
+
         dtype = kwargs.pop("dtype", None)
 
         embeddings: List = []
@@ -319,7 +319,7 @@ async def aembed(
 
         if preprocess:
             text = preprocess(text)
-        
+
         dtype = kwargs.pop("dtype", None)
 
         result = await self._aclient.embeddings.create(input=[text], model=self.model)

redisvl/utils/vectorize/text/cohere.py

@@ -155,12 +155,12 @@ def embed(
                 "Must pass in a str value for cohere embedding input_type. \
                     See https://docs.cohere.com/reference/embed."
             )
-        
+
         if preprocess:
             text = preprocess(text)
-        
+
         dtype = kwargs.pop("dtype", None)
-    
+
         embedding = self._client.embed(
             texts=[text], model=self.model, input_type=input_type
         ).embeddings[0]
@@ -227,7 +227,7 @@ def embed_many(
                 "Must pass in a str value for cohere embedding input_type.\
                     See https://docs.cohere.com/reference/embed."
             )
-        
+
         dtype = kwargs.pop("dtype", None)
 
         embeddings: List = []

redisvl/utils/vectorize/text/custom.py

@@ -211,7 +211,7 @@ def embed_many(
 
         if not self._embed_many_func:
             raise NotImplementedError
-        
+
         dtype = kwargs.pop("dtype", None)
 
         embeddings: List = []
@@ -253,7 +253,7 @@ async def aembed(
 
         if preprocess:
             text = preprocess(text)
-        
+
         dtype = kwargs.pop("dtype", None)
 
         result = await self._aembed_func(text, **kwargs)
@@ -292,9 +292,9 @@ async def aembed_many(
 
         if not self._aembed_many_func:
             raise NotImplementedError
-        
+
         dtype = kwargs.pop("dtype", None)
-        
+
         embeddings: List = []
         for batch in self.batchify(texts, batch_size, preprocess):
             results = await self._aembed_many_func(batch, **kwargs)

redisvl/utils/vectorize/text/huggingface.py

@@ -135,7 +135,7 @@ def embed_many(
             raise TypeError("Must pass in a list of str values to embed.")
         if len(texts) > 0 and not isinstance(texts[0], str):
             raise TypeError("Must pass in a list of str values to embed.")
-        
+
         dtype = kwargs.pop("dtype", None)
 
         embeddings: List = []

redisvl/utils/vectorize/text/mistral.py

@@ -139,9 +139,9 @@ def embed_many(
             raise TypeError("Must pass in a list of str values to embed.")
         if len(texts) > 0 and not isinstance(texts[0], str):
             raise TypeError("Must pass in a list of str values to embed.")
-        
+
         dtype = kwargs.pop("dtype", None)
-        
+
         embeddings: List = []
         for batch in self.batchify(texts, batch_size, preprocess):
             response = self._client.embeddings(model=self.model, input=batch)
@@ -183,7 +183,7 @@ def embed(
 
         if preprocess:
             text = preprocess(text)
-        
+
         dtype = kwargs.pop("dtype", None)
 
         result = self._client.embeddings(model=self.model, input=[text])
@@ -223,7 +223,7 @@ async def aembed_many(
             raise TypeError("Must pass in a list of str values to embed.")
         if len(texts) > 0 and not isinstance(texts[0], str):
             raise TypeError("Must pass in a list of str values to embed.")
-        
+
         dtype = kwargs.pop("dtype", None)
 
         embeddings: List = []
@@ -267,7 +267,7 @@ async def aembed(
 
         if preprocess:
             text = preprocess(text)
-        
+
         dtype = kwargs.pop("dtype", None)
 
         result = await self._aclient.embeddings(model=self.model, input=[text])

redisvl/utils/vectorize/text/openai.py

@@ -143,9 +143,9 @@ def embed_many(
             raise TypeError("Must pass in a list of str values to embed.")
         if len(texts) > 0 and not isinstance(texts[0], str):
             raise TypeError("Must pass in a list of str values to embed.")
-        
+
         dtype = kwargs.pop("dtype", None)
-        
+
         embeddings: List = []
         for batch in self.batchify(texts, batch_size, preprocess):
             response = self._client.embeddings.create(input=batch, model=self.model)
@@ -187,7 +187,7 @@ def embed(
 
         if preprocess:
             text = preprocess(text)
-        
+
         dtype = kwargs.pop("dtype", None)
 
         result = self._client.embeddings.create(input=[text], model=self.model)
@@ -227,7 +227,7 @@ async def aembed_many(
             raise TypeError("Must pass in a list of str values to embed.")
         if len(texts) > 0 and not isinstance(texts[0], str):
             raise TypeError("Must pass in a list of str values to embed.")
-        
+
         dtype = kwargs.pop("dtype", None)
 
         embeddings: List = []
@@ -273,7 +273,7 @@ async def aembed(
 
         if preprocess:
             text = preprocess(text)
-        
+
         dtype = kwargs.pop("dtype", None)
 
         result = await self._aclient.embeddings.create(input=[text], model=self.model)

redisvl/utils/vectorize/text/vertexai.py

@@ -150,9 +150,9 @@ def embed_many(
             raise TypeError("Must pass in a list of str values to embed.")
         if len(texts) > 0 and not isinstance(texts[0], str):
             raise TypeError("Must pass in a list of str values to embed.")
-        
+
         dtype = kwargs.pop("dtype", None)
-        
+
         embeddings: List = []
         for batch in self.batchify(texts, batch_size, preprocess):
             response = self._client.get_embeddings(batch)
@@ -193,7 +193,7 @@ def embed(
 
         if preprocess:
             text = preprocess(text)
-        
+
         dtype = kwargs.pop("dtype", None)
 
         result = self._client.get_embeddings([text])