🔨 refactor(utils.py): extract build_input_keys_response function to improve code reusability

🚀 feat(chat.py): use build_input_keys_response function to build input keys response
The build_input_keys_response function was extracted from the remove_api_keys function to improve code reusability. It builds the input keys response object, which contains the input keys and memory keys of a langchain object. The chat.py file now uses this function to build the input keys response object instead of building it manually. This improves code readability and reduces code duplication.

src/backend/langflow/api/utils.py

@@ -22,3 +22,26 @@ def remove_api_keys(flow: dict):
                     value["value"] = None
 
     return flow
+
+
+def build_input_keys_response(langchain_object):
+    """Build the input keys response."""
+    input_keys_response = {
+        "input_keys": langchain_object.input_keys,
+        "memory_keys": [],
+    }
+    # If the object has memory, that memory will have a memory_variables attribute
+    # memory variables should be removed from the input keys
+    if hasattr(langchain_object, "memory") and hasattr(
+        langchain_object.memory, "memory_variables"
+    ):
+        # Remove memory variables from input keys
+        input_keys_response["input_keys"] = [
+            key
+            for key in input_keys_response["input_keys"]
+            if key not in langchain_object.memory.memory_variables
+        ]
+        # Add memory variables to memory_keys
+        input_keys_response["memory_keys"] = langchain_object.memory.memory_variables
+
+    return input_keys_response

src/backend/langflow/api/v1/chat.py

@@ -6,6 +6,7 @@ from fastapi import (
     status,
 )
 from fastapi.responses import StreamingResponse
+from langflow.api.utils import build_input_keys_response
 from langflow.api.v1.schemas import BuiltResponse, InitResponse, StreamData
 
 from langflow.chat.manager import ChatManager
@@ -123,9 +124,7 @@ async def stream_build(flow_id: str):
             langchain_object = graph.build()
             # Now we  need to check the input_keys to send them to the client
             if hasattr(langchain_object, "input_keys"):
-                input_keys_response = {
-                    "input_keys": langchain_object.input_keys,
-                }
+                input_keys_response = build_input_keys_response(langchain_object)
                 yield str(StreamData(event="input_keys", data=input_keys_response))
 
             chat_manager.set_cache(flow_id, langchain_object)