🐛 fix(endpoints.py): change import statement for typing module to import Annotated from typing module to fix type hinting error

✨ feat(endpoints.py): add support for clear_cache parameter in process_flow endpoint to allow clearing the cache before processing the flow
🐛 fix(process.py): add logic to clear cache if clear_cache parameter is True in process_graph_cached function to fix caching issue

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

@@ -1,5 +1,5 @@
 from http import HTTPStatus
-from typing import Optional
+from typing import Annotated, Optional
 
 from langflow.cache.utils import save_uploaded_file
 from langflow.database.models.flow import Flow
@@ -7,7 +7,7 @@ from langflow.processing.process import process_graph_cached, process_tweaks
 from langflow.utils.logger import logger
 from langflow.settings import settings
 
-from fastapi import APIRouter, Depends, HTTPException, UploadFile
+from fastapi import APIRouter, Depends, HTTPException, UploadFile, Body
 
 from langflow.interface.custom.custom_component import CustomComponent
 
@@ -96,6 +96,7 @@ async def process_flow(
     flow_id: str,
     inputs: Optional[dict] = None,
     tweaks: Optional[dict] = None,
+    clear_cache: Annotated[bool, Body(embed=True)] = False,  # noqa: F821
     session: Session = Depends(get_session),
 ):
     """
@@ -115,7 +116,7 @@ async def process_flow(
                 graph_data = process_tweaks(graph_data, tweaks)
             except Exception as exc:
                 logger.error(f"Error processing tweaks: {exc}")
-        response = process_graph_cached(graph_data, inputs)
+        response = process_graph_cached(graph_data, inputs, clear_cache)
         return ProcessResponse(
             result=response,
         )

src/backend/langflow/processing/process.py

@@ -85,12 +85,17 @@ def get_input_str_if_only_one_input(inputs: dict) -> Optional[str]:
     return list(inputs.values())[0] if len(inputs) == 1 else None
 
 
-def process_graph_cached(data_graph: Dict[str, Any], inputs: Optional[dict] = None):
+def process_graph_cached(
+    data_graph: Dict[str, Any], inputs: Optional[dict] = None, clear_cache=False
+):
     """
     Process graph by extracting input variables and replacing ZeroShotPrompt
     with PromptTemplate,then run the graph and return the result and thought.
     """
     # Load langchain object
+    if clear_cache:
+        build_sorted_vertices_with_caching.clear_cache()
+        logger.debug("Cleared cache")
     langchain_object, artifacts = build_sorted_vertices_with_caching(data_graph)
     logger.debug("Loaded LangChain object")
     if inputs is None: