🐛 fix(vertex/base.py): add input types from template_dicts to optional_inputs

🐛 fix(interface/initialize/loading.py): handle BaseOutputParser instances in instantiate_prompt
The `optional_inputs` list in the `Vertex` class is now extended with the `input_types` from the `template_dicts` dictionary. This ensures that the `optional_inputs` list contains all the input types that are not required. In the `instantiate_prompt` function, if the `variable` is an instance of `BaseOutputParser` and has a `get_format_instructions` method, the `format_kwargs` dictionary is updated with the result of calling `get_format_instructions()`. This allows handling `BaseOutputParser` instances correctly in the `instantiate_prompt` function.

src/backend/langflow/interface/initialize/loading.py

@@ -7,7 +7,8 @@ from langchain.agents.agent import AgentExecutor
 from langchain.agents.agent_toolkits.base import BaseToolkit
 from langchain.agents.tools import BaseTool
 from langflow.interface.initialize.vector_store import vecstore_initializer
-from langchain.schema import Document
+
+from langchain.schema import Document, BaseOutputParser
 from pydantic import ValidationError
 
 from langflow.interface.custom_lists import CUSTOM_NODES
@@ -130,6 +131,10 @@ def instantiate_prompt(node_type, class_object, params):
             variable = params[input_variable]
             if isinstance(variable, str):
                 format_kwargs[input_variable] = variable
+            elif isinstance(variable, BaseOutputParser) and hasattr(
+                variable, "get_format_instructions"
+            ):
+                format_kwargs[input_variable] = variable.get_format_instructions()
             # check if is a list of Document
             elif isinstance(variable, List) and all(
                 isinstance(item, Document) for item in variable