styling and linting

Author: tylerhutcherson

redisvl/vectorize/base.py

@@ -1,6 +1,8 @@
 from typing import Callable, Dict, List, Optional
+
 from redisvl.utils.utils import array_to_buffer
 
+
 class BaseVectorizer:
     def __init__(self, model: str, dims: int, api_config: Optional[Dict] = None):
         self._dims = dims
@@ -24,15 +26,15 @@ def embed_many(
         texts: List[str],
         preprocess: Optional[Callable] = None,
         batch_size: Optional[int] = 1000,
-        as_buffer: Optional[bool] = False
+        as_buffer: Optional[bool] = False,
     ) -> List[List[float]]:
         raise NotImplementedError
 
     def embed(
         self,
         text: str,
         preprocess: Optional[Callable] = None,
-        as_buffer: Optional[bool] = False
+        as_buffer: Optional[bool] = False,
     ) -> List[float]:
         raise NotImplementedError
 
@@ -41,15 +43,15 @@ async def aembed_many(
         texts: List[str],
         preprocess: Optional[Callable] = None,
         batch_size: Optional[int] = 1000,
-        as_buffer: Optional[bool] = False
+        as_buffer: Optional[bool] = False,
     ) -> List[List[float]]:
         raise NotImplementedError
 
     async def aembed(
         self,
         text: str,
         preprocess: Optional[Callable] = None,
-        as_buffer: Optional[bool] = False
+        as_buffer: Optional[bool] = False,
     ) -> List[float]:
         raise NotImplementedError
 

redisvl/vectorize/text/huggingface.py

@@ -22,7 +22,7 @@ def embed(
         self,
         text: str,
         preprocess: Optional[Callable] = None,
-        as_buffer: Optional[float] = False
+        as_buffer: Optional[float] = False,
     ) -> List[float]:
         """Embed a chunk of text using the Hugging Face sentence transformer.
 
@@ -46,7 +46,7 @@ def embed_many(
         texts: List[str],
         preprocess: Optional[Callable] = None,
         batch_size: int = 1000,
-        as_buffer: Optional[float] = None
+        as_buffer: Optional[float] = None,
     ) -> List[List[float]]:
         """Asynchronously embed many chunks of texts using the Hugging Face sentence
         transformer.
@@ -66,7 +66,10 @@ def embed_many(
         embeddings: List = []
         for batch in self.batchify(texts, batch_size, preprocess):
             batch_embeddings = self._model_client.encode(batch)
-            embeddings.extend([
-                self._process_embedding(embedding.tolist(), as_buffer) for embedding in batch_embeddings
-            ])
+            embeddings.extend(
+                [
+                    self._process_embedding(embedding.tolist(), as_buffer)
+                    for embedding in batch_embeddings
+                ]
+            )
         return embeddings

redisvl/vectorize/text/openai.py

@@ -1,9 +1,10 @@
 from typing import Callable, Dict, List, Optional
-from tenacity import (
+
+from tenacity import (  # for exponential backoff
     retry,
     stop_after_attempt,
     wait_random_exponential,
-)  # for exponential backoff
+)
 
 from redisvl.vectorize.base import BaseVectorizer
 
@@ -30,7 +31,7 @@ def embed_many(
         texts: List[str],
         preprocess: Optional[Callable] = None,
         batch_size: Optional[int] = 10,
-        as_buffer: Optional[float] = False
+        as_buffer: Optional[float] = False,
     ) -> List[List[float]]:
         """Embed many chunks of texts using the OpenAI API.
 
@@ -50,7 +51,8 @@ def embed_many(
         for batch in self.batchify(texts, batch_size, preprocess):
             response = self._model_client.create(input=batch, engine=self._model)
             embeddings += [
-                self._process_embedding(r["embedding"], as_buffer) for r in response["data"]
+                self._process_embedding(r["embedding"], as_buffer)
+                for r in response["data"]
             ]
         return embeddings
 
@@ -59,7 +61,7 @@ def embed(
         self,
         text: str,
         preprocess: Optional[Callable] = None,
-        as_buffer: Optional[float] = False
+        as_buffer: Optional[float] = False,
     ) -> List[float]:
         """Embed a chunk of text using the OpenAI API.
 
@@ -84,7 +86,7 @@ async def aembed_many(
         texts: List[str],
         preprocess: Optional[Callable] = None,
         batch_size: int = 1000,
-        as_buffer: Optional[bool] = False
+        as_buffer: Optional[bool] = False,
     ) -> List[List[float]]:
         """Asynchronously embed many chunks of texts using the OpenAI API.
 
@@ -104,7 +106,8 @@ async def aembed_many(
         for batch in self.batchify(texts, batch_size, preprocess):
             response = await self._model_client.acreate(input=batch, engine=self._model)
             embeddings += [
-                self._process_embedding(r["embedding"], as_buffer) for r in response["data"]
+                self._process_embedding(r["embedding"], as_buffer)
+                for r in response["data"]
             ]
         return embeddings
 
@@ -113,7 +116,7 @@ async def aembed(
         self,
         text: str,
         preprocess: Optional[Callable] = None,
-        as_buffer: Optional[bool] = False
+        as_buffer: Optional[bool] = False,
     ) -> List[float]:
         """Asynchronously embed a chunk of text using the OpenAI API.