diff --git a/src/backend/base/langflow/alembic/helpers/flow.py b/src/backend/base/langflow/alembic/helpers/flow.py
index 328e99ca1..cfd3272d9 100644
--- a/src/backend/base/langflow/alembic/helpers/flow.py
+++ b/src/backend/base/langflow/alembic/helpers/flow.py
@@ -178,7 +178,7 @@ def get_flow_inputs(graph: "Graph") -> List["Vertex"]:
     return inputs
 
 
-def build_schema_from_inputs(name: str, inputs: List[tuple[str, str, str]]) -> BaseModel:
+def build_schema_from_inputs(name: str, inputs: List["Vertex"]) -> BaseModel:
     """
     Builds a schema from the given inputs.
 
diff --git a/src/backend/base/langflow/components/agents/XMLAgent.py b/src/backend/base/langflow/components/agents/XMLAgent.py
index efd5cc010..687bfff6f 100644
--- a/src/backend/base/langflow/components/agents/XMLAgent.py
+++ b/src/backend/base/langflow/components/agents/XMLAgent.py
@@ -1,4 +1,4 @@
-from typing import List
+from typing import List, Optional
 
 from langchain.agents import create_xml_agent
 from langchain_core.prompts import PromptTemplate
@@ -69,7 +69,7 @@ class XMLAgentComponent(LCAgentComponent):
         llm: BaseLLM,
         tools: List[Tool],
         prompt: str,
-        memory: BaseMemory = None,
+        memory: Optional[BaseMemory] = None,
         tool_template: str = "{name}: {description}",
         handle_parsing_errors: bool = True,
     ) -> Text:
diff --git a/src/backend/base/langflow/components/models/AzureOpenAIModel.py b/src/backend/base/langflow/components/models/AzureOpenAIModel.py
index eeb488f03..58cc3f2c2 100644
--- a/src/backend/base/langflow/components/models/AzureOpenAIModel.py
+++ b/src/backend/base/langflow/components/models/AzureOpenAIModel.py
@@ -2,7 +2,6 @@ from typing import Optional
 
 from langchain.llms.base import BaseLanguageModel
 from langchain_openai import AzureChatOpenAI
-from pydantic.v1 import SecretStr
 
 from langflow.base.models.model import LCModelComponent
 from langflow.field_typing import Text
@@ -91,21 +90,20 @@ class AzureChatOpenAIComponent(LCModelComponent):
         azure_endpoint: str,
         input_value: Text,
         azure_deployment: str,
-        api_key: str,
         api_version: str,
+        api_key: Optional[str] = None,
         system_message: Optional[str] = None,
         temperature: float = 0.7,
         max_tokens: Optional[int] = 1000,
         stream: bool = False,
     ) -> BaseLanguageModel:
-        secret_api_key = SecretStr(api_key)
         try:
             output = AzureChatOpenAI(
                 model=model,
                 azure_endpoint=azure_endpoint,
                 azure_deployment=azure_deployment,
                 api_version=api_version,
-                api_key=secret_api_key,
+                api_key=api_key,
                 temperature=temperature,
                 max_tokens=max_tokens,
             )
diff --git a/src/backend/base/langflow/components/vectorstores/AstraDBSearch.py b/src/backend/base/langflow/components/vectorstores/AstraDBSearch.py
index 8432f01f1..e358eeae4 100644
--- a/src/backend/base/langflow/components/vectorstores/AstraDBSearch.py
+++ b/src/backend/base/langflow/components/vectorstores/AstraDBSearch.py
@@ -74,7 +74,7 @@ class AstraDBSearchComponent(AstraDBVectorStoreComponent, LCVectorStoreComponent
         self,
         embedding: Embeddings,
         collection_name: str,
-        input_value: Optional[Text] = None,
+        input_value: Text,
         search_type: str = "Similarity",
         token: Optional[str] = None,
         api_endpoint: Optional[str] = None,
diff --git a/src/backend/base/langflow/components/vectorstores/MongoDBAtlasVectorSearch.py b/src/backend/base/langflow/components/vectorstores/MongoDBAtlasVectorSearch.py
index 10b69ce15..9ccd80a27 100644
--- a/src/backend/base/langflow/components/vectorstores/MongoDBAtlasVectorSearch.py
+++ b/src/backend/base/langflow/components/vectorstores/MongoDBAtlasVectorSearch.py
@@ -38,7 +38,7 @@ class MongoDBAtlasSearchComponent(MongoDBAtlasComponent, LCVectorStoreComponent)
     ) -> List[Record]:
         search_kwargs = search_kwargs or {}
         vector_store = super().build(
-            connection_string=mongodb_atlas_cluster_uri,
+            mongodb_atlas_cluster_uri=mongodb_atlas_cluster_uri,
             namespace=f"{db_name}.{collection_name}",
             embedding=embedding,
             index_name=index_name,