diff --git a/src/vanna/pgvector/pgvector.py b/src/vanna/pgvector/pgvector.py
index cf0c2a23..3cddeb46 100644
--- a/src/vanna/pgvector/pgvector.py
+++ b/src/vanna/pgvector/pgvector.py
@@ -28,20 +28,20 @@ def __init__(self, config=None):
         if config and "embedding_function" in config:
             self.embedding_function = config.get("embedding_function")
         else:
-            from sentence_transformers import SentenceTransformer
-            self.embedding_function = SentenceTransformer("sentence-transformers/all-MiniLM-l6-v2")
+            from langchain_huggingface import HuggingFaceEmbeddings
+            self.embedding_function = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
 
-        self.sql_vectorstore = PGVector(
+        self.sql_collection = PGVector(
             embeddings=self.embedding_function,
             collection_name="sql",
             connection=self.connection_string,
         )
-        self.ddl_vectorstore = PGVector(
+        self.ddl_collection = PGVector(
             embeddings=self.embedding_function,
             collection_name="ddl",
             connection=self.connection_string,
         )
-        self.documentation_vectorstore = PGVector(
+        self.documentation_collection = PGVector(
             embeddings=self.embedding_function,
             collection_name="documentation",
             connection=self.connection_string,
@@ -94,16 +94,16 @@ def get_collection(self, collection_name):
             case _:
                 raise ValueError("Specified collection does not exist.")
 
-    async def get_similar_question_sql(self, question: str) -> list:
+    def get_similar_question_sql(self, question: str) -> list:
         documents = self.sql_collection.similarity_search(query=question, k=self.n_results)
         return [ast.literal_eval(document.page_content) for document in documents]
 
-    async def get_related_ddl(self, question: str, **kwargs) -> list:
-        documents = await self.ddl_collection.similarity_search(query=question, k=self.n_results)
+    def get_related_ddl(self, question: str, **kwargs) -> list:
+        documents = self.ddl_collection.similarity_search(query=question, k=self.n_results)
         return [document.page_content for document in documents]
 
-    async def get_related_documentation(self, question: str, **kwargs) -> list:
-        documents = await self.documentation_collection.similarity_search(query=question, k=self.n_results)
+    def get_related_documentation(self, question: str, **kwargs) -> list:
+        documents = self.documentation_collection.similarity_search(query=question, k=self.n_results)
         return [document.page_content for document in documents]
 
     def train(
@@ -251,15 +251,3 @@ def remove_collection(self, collection_name: str) -> bool:
 
     def generate_embedding(self, *args, **kwargs):
         pass
-
-    def submit_prompt(self, *args, **kwargs):
-        pass
-
-    def system_message(self, message: str) -> any:
-        return {"role": "system", "content": message}
-
-    def user_message(self, message: str) -> any:
-        return {"role": "user", "content": message}
-
-    def assistant_message(self, message: str) -> any:
-        return {"role": "assistant", "content": message}
diff --git a/tests/test_pgvector.py b/tests/test_pgvector.py
index 8c9344a5..2d82f273 100644
--- a/tests/test_pgvector.py
+++ b/tests/test_pgvector.py
@@ -3,22 +3,47 @@
 from dotenv import load_dotenv
 
 # from vanna.pgvector import PG_VectorStore
+# from vanna.openai import OpenAI_Chat
 
+# assume .env file placed next to file with provided env vars
 load_dotenv()
 
-# Removing thiese tests for now until the dependencies are sorted out
 # def get_vanna_connection_string():
 #     server = os.environ.get("PG_SERVER")
 #     driver = "psycopg"
-#     port = 5434
+#     port = os.environ.get("PG_PORT", 5432)
 #     database = os.environ.get("PG_DATABASE")
 #     username = os.environ.get("PG_USERNAME")
 #     password = os.environ.get("PG_PASSWORD")
 
-#     return f"postgresql+psycopg://{username}:{password}@{server}:{port}/{database}"
+# def test_pgvector_e2e():
+#     # configure Vanna to use OpenAI and PGVector
+#     class VannaCustom(PG_VectorStore, OpenAI_Chat):
+#         def __init__(self, config=None):
+#             PG_VectorStore.__init__(self, config=config)
+#             OpenAI_Chat.__init__(self, config=config)
+    
+#     vn = VannaCustom(config={
+#         'api_key': os.environ['OPENAI_API_KEY'],
+#         'model': 'gpt-3.5-turbo',
+#         "connection_string": get_vanna_connection_string(),
+#     })
 
+#     # connect to SQLite database
+#     vn.connect_to_sqlite('https://vanna.ai/Chinook.sqlite')
+
+#     # train Vanna on DDLs
+#     df_ddl = vn.run_sql("SELECT type, sql FROM sqlite_master WHERE sql is not null")
+#     for ddl in df_ddl['sql'].to_list():
+#         vn.train(ddl=ddl)
+#     assert len(vn.get_related_ddl("dummy question")) == 10  # assume 10 DDL chunks are retrieved by default
+    
+#     question = "What are the top 7 customers by sales?"
+#     sql = vn.generate_sql(question)
+#     df = vn.run_sql(sql)
+#     assert len(df) == 7
+
+#     # test if Vanna can generate an answer
+#     answer = vn.ask(question)
+#     assert answer is not None
 
-# def test_pgvector():
-#     connection_string = get_vanna_connection_string()
-#     pgclient = PG_VectorStore(config={"connection_string": connection_string})
-#     assert pgclient is not None