Improvement[Community]Improve Embeddings API

libs/community/langchain_community/embeddings/baichuan.py

@@ -15,7 +15,7 @@
 from requests import RequestException
 from typing_extensions import Self
 
-BAICHUAN_API_URL: str = "http://api.baichuan-ai.com/v1/embeddings"
+BAICHUAN_API_URL: str = "https://api.baichuan-ai.com/v1/embeddings"
 
 # BaichuanTextEmbeddings is an embedding model provided by Baichuan Inc. (https://www.baichuan-ai.com/home).
 # As of today (Jan 25th, 2024) BaichuanTextEmbeddings ranks #1 in C-MTEB
@@ -25,7 +25,7 @@
 # Official Website: https://platform.baichuan-ai.com/docs/text-Embedding
 # An API-key is required to use this embedding model. You can get one by registering
 # at https://platform.baichuan-ai.com/docs/text-Embedding.
-# BaichuanTextEmbeddings support 512 token window and preduces vectors with
+# BaichuanTextEmbeddings support 512 token window and produces vectors with
 # 1024 dimensions.
 
 

libs/community/langchain_community/embeddings/dashscope.py

@@ -143,8 +143,6 @@ def embed_documents(self, texts: List[str]) -> List[List[float]]:
 
         Args:
             texts: The list of texts to embed.
-            chunk_size: The chunk size of embeddings. If None, will use the chunk size
-                specified by the class.
 
         Returns:
             List of embeddings, one for each text.

libs/community/langchain_community/embeddings/fake.py

@@ -36,7 +36,8 @@ def _get_embedding(self, seed: int) -> List[float]:
         np.random.seed(seed)
         return list(np.random.normal(size=self.size))
 
-    def _get_seed(self, text: str) -> int:
+    @staticmethod
+    def _get_seed(text: str) -> int:
         """
         Get a seed for the random generator, using the hash of the text.
         """