[Bug fix] Fix embedding issue for opensearch and some other vector databases (#1163)

embedchain/vectordb/weaviate.py

@@ -1,6 +1,6 @@
 import copy
 import os
-from typing import Any, Optional, Union
+from typing import Optional, Union
 
 try:
     import weaviate
@@ -151,17 +151,8 @@ class WeaviateDB(BaseVectorDB):
 
         return {"ids": existing_ids}
 
-    def add(
-        self,
-        embeddings: list[list[float]],
-        documents: list[str],
-        metadatas: list[object],
-        ids: list[str],
-        **kwargs: Optional[dict[str, any]],
-    ):
+    def add(self, documents: list[str], metadatas: list[object], ids: list[str], **kwargs: Optional[dict[str, any]]):
         """add data in vector database
-        :param embeddings: list of embeddings for the corresponding documents to be added
-        :type documents: list[list[float]]
         :param documents: list of texts to add
         :type documents: list[str]
         :param metadatas: list of metadata associated with docs
@@ -191,12 +182,7 @@ class WeaviateDB(BaseVectorDB):
                 )
 
     def query(
-        self,
-        input_query: list[str],
-        n_results: int,
-        where: dict[str, any],
-        citations: bool = False,
-        **kwargs: Optional[dict[str, Any]],
+        self, input_query: list[str], n_results: int, where: dict[str, any], citations: bool = False
     ) -> Union[list[tuple[str, dict]], list[str]]:
         """
         query contents from vector database based on vector similarity