docs: fix kwargs docstring (#25010)

Fix:
![Screenshot 2024-08-02 at 5 33 37
PM](https://github.com/user-attachments/assets/7c56cdeb-ee81-454c-b3eb-86aa8a9bdc8d)

libs/community/langchain_community/vectorstores/aerospike.py

@@ -178,7 +178,7 @@ class Aerospike(VectorStore):
             wait_for_index: If True, wait for the all the texts to be indexed
                 before returning. Requires index_name to be provided. Defaults
                 to True.
-            **kwargs: Additional keyword arguments to pass to the client upsert call.
+            kwargs: Additional keyword arguments to pass to the client upsert call.
 
         Returns:
             List of ids from adding the texts into the vectorstore.

libs/community/langchain_community/vectorstores/aperturedb.py

@@ -396,7 +396,7 @@ class ApertureDB(VectorStore):
             texts: List of text strings
             embedding: Embeddings object as for constructing the vectorstore
             metadatas: Optional list of metadatas associated with the texts.
-            **kwargs: Additional arguments to pass to the constructor
+            kwargs: Additional arguments to pass to the constructor
         """
         store = cls(embeddings=embedding, **kwargs)
         store.add_texts(texts, metadatas)
@@ -416,7 +416,7 @@ class ApertureDB(VectorStore):
             documents: List of Document objects
             embedding: Embeddings object as for constructing the vectorstore
             metadatas: Optional list of metadatas associated with the texts.
-            **kwargs: Additional arguments to pass to the constructor
+            kwargs: Additional arguments to pass to the constructor
         """
         store = cls(embeddings=embedding, **kwargs)
         store.add_documents(documents)

libs/community/langchain_community/vectorstores/clarifai.py

@@ -272,7 +272,7 @@ class Clarifai(VectorStore):
             token (Optional[str], optional): Session token. Defaults to None.
             metadatas (Optional[List[dict]]): Optional list
             of metadatas. Defaults to None.
-            **kwargs: Additional keyword arguments to be passed to the Search.
+            kwargs: Additional keyword arguments to be passed to the Search.
 
         Returns:
             Clarifai: Clarifai vectorstore.
@@ -310,7 +310,7 @@ class Clarifai(VectorStore):
             to return during vector search. Defaults to None.
             pat (Optional[str], optional): Personal access token. Defaults to None.
             token (Optional[str], optional): Session token. Defaults to None.
-            **kwargs: Additional keyword arguments to be passed to the Search.
+            kwargs: Additional keyword arguments to be passed to the Search.
 
         Returns:
             Clarifai: Clarifai vectorstore.

libs/community/langchain_community/vectorstores/deeplake.py

@@ -385,7 +385,7 @@ class DeepLake(VectorStore):
                 model was trained. The search is performed using the Deep Memory model.
                 If False, the distance metric is set to "COS" or whatever distance
                 metric user specifies.
-            **kwargs: Additional keyword arguments.
+            kwargs: Additional keyword arguments.
 
         Returns:
             List of Documents by the specified distance metric,
@@ -505,7 +505,7 @@ class DeepLake(VectorStore):
         Args:
             k (int): Number of Documents to return. Defaults to 4.
             query (str): Text to look up similar documents.
-            **kwargs: Additional keyword arguments include:
+            kwargs: Additional keyword arguments include:
                 embedding (Callable): Embedding function to use. Defaults to None.
                 distance_metric (str): 'L2' for Euclidean, 'L1' for Nuclear, 'max'
                     for L-infinity, 'cos' for cosine, 'dot' for dot product.
@@ -567,7 +567,7 @@ class DeepLake(VectorStore):
             embedding (Union[List[float], np.ndarray]):
                 Embedding to find similar docs.
             k (int): Number of Documents to return. Defaults to 4.
-            **kwargs: Additional keyword arguments including:
+            kwargs: Additional keyword arguments including:
                 filter (Union[Dict, Callable], optional):
                     Additional filter before embedding search.
                     - ``Dict`` - Key-value search on tensors of htype json. True
@@ -636,7 +636,7 @@ class DeepLake(VectorStore):
         Args:
             query (str): Query text to search for.
             k (int): Number of results to return. Defaults to 4.
-            **kwargs: Additional keyword arguments. Some of these arguments are:
+            kwargs: Additional keyword arguments. Some of these arguments are:
                 distance_metric: `L2` for Euclidean, `L1` for Nuclear, `max` L-infinity
                     distance, `cos` for cosine similarity, 'dot' for dot product.
                     Defaults to `L2`.
@@ -728,7 +728,7 @@ class DeepLake(VectorStore):
                 which the model was trained. The search is performed using the Deep
                 Memory model. If False, the distance metric is set to "COS" or
                 whatever distance metric user specifies.
-            **kwargs: Additional keyword arguments.
+            kwargs: Additional keyword arguments.
 
         Returns:
             List[Documents] - A list of documents.
@@ -795,7 +795,7 @@ class DeepLake(VectorStore):
                 which the model was trained. The search is performed using the Deep
                 Memory model. If False, the distance metric is set to "COS" or
                 whatever distance metric user specifies.
-            **kwargs: Additional keyword arguments
+            kwargs: Additional keyword arguments
 
         Returns:
             List of Documents selected by maximal marginal relevance.
@@ -866,7 +866,7 @@ class DeepLake(VectorStore):
                 Note, in other places, it is called embedding_function.
             metadatas (Optional[List[dict]]): List of metadatas. Defaults to None.
             ids (Optional[List[str]]): List of document IDs. Defaults to None.
-            **kwargs: Additional keyword arguments.
+            kwargs: Additional keyword arguments.
 
         Returns:
             DeepLake: Deep Lake dataset.

libs/community/langchain_community/vectorstores/duckdb.py

@@ -255,7 +255,7 @@ class DuckDB(VectorStore):
             texts: List of strings to add to the vector store.
             embedding: The embedding function or model to use for generating embeddings.
             metadatas: Optional list of metadata dictionaries associated with the texts.
-            **kwargs: Additional keyword arguments including:
+            kwargs: Additional keyword arguments including:
                 - connection: DuckDB connection. If not provided, a new connection will
                   be created.
                 - vector_key: The column name for storing vectors. Default "vector".

libs/community/langchain_community/vectorstores/tidb_vector.py

@@ -285,7 +285,7 @@ class TiDBVectorStore(VectorStore):
 
         Args:
             ids (Optional[List[str]]): A list of vector IDs to delete.
-            **kwargs: Additional keyword arguments.
+            kwargs: Additional keyword arguments.
         """
 
         self._tidb.delete(ids=ids, **kwargs)
@@ -305,7 +305,7 @@ class TiDBVectorStore(VectorStore):
             k (int, optional): The number of results to retrieve. Defaults to 4.
             filter (dict, optional): A filter to apply to the search results.
                 Defaults to None.
-            **kwargs: Additional keyword arguments.
+            kwargs: Additional keyword arguments.
 
         Returns:
             List[Document]: A list of Document objects representing the search results.
@@ -328,7 +328,7 @@ class TiDBVectorStore(VectorStore):
             k (int, optional): The number of results to return. Defaults to 5.
             filter (dict, optional): A filter to apply to the search results.
                 Defaults to None.
-            **kwargs: Additional keyword arguments.
+            kwargs: Additional keyword arguments.
 
         Returns:
             A list of tuples containing relevant documents and their similarity scores.

libs/community/langchain_community/vectorstores/weaviate.py

@@ -428,7 +428,7 @@ class Weaviate(VectorStore):
             relevance_score_fn: Function for converting whatever distance function the
                 vector store uses to a relevance score, which is a normalized similarity
                 score (0 means dissimilar, 1 means similar).
-            **kwargs: Additional named parameters to pass to ``Weaviate.__init__()``.
+            kwargs: Additional named parameters to pass to ``Weaviate.__init__()``.
 
         Example:
             .. code-block:: python

libs/community/langchain_community/vectorstores/zep.py

@@ -639,7 +639,7 @@ class ZepVectorStore(VectorStore):
             api_url (str): The URL of the Zep API.
             api_key (Optional[str]): The API key for the Zep API.
             config (Optional[CollectionConfig]): The configuration for the collection.
-            **kwargs: Additional parameters specific to the vectorstore.
+            kwargs: Additional parameters specific to the vectorstore.
 
         Returns:
             ZepVectorStore: An instance of ZepVectorStore.

libs/community/langchain_community/vectorstores/zep_cloud.py

@@ -442,7 +442,7 @@ class ZepCloudVectorStore(VectorStore):
                associated with the texts.
             collection_name (str): The name of the collection in the Zep store.
             api_key (str): The API key for the Zep API.
-            **kwargs: Additional parameters specific to the vectorstore.
+            kwargs: Additional parameters specific to the vectorstore.
 
         Returns:
             ZepVectorStore: An instance of ZepVectorStore.