🔧 fix(process.py): refactor process_graph_cached function for better readability and maintainability

✨ feat(process.py): add clear_caches_if_needed function to clear cache if clear_cache flag is set
✨ feat(process.py): add load_langchain_object function to load langchain_object and artifacts from data_graph
✨ feat(process.py): add process_inputs function to process inputs and add artifacts to inputs
✨ feat(process.py): add generate_result function to generate result and thought based on langchain_object and inputs

src/backend/langflow/processing/process.py

@@ -101,45 +101,39 @@ def get_build_result(data_graph, session_id):
     return build_sorted_vertices_with_caching(data_graph)
 
 
-def process_graph_cached(
+def clear_caches_if_needed(clear_cache: bool):
-    data_graph: Dict[str, Any],
-    inputs: Optional[dict] = None,
-    clear_cache=False,
-    session_id=None,
-):
-    """
-    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")
 
+
+def load_langchain_object(
+    data_graph: Dict[str, Any], session_id: str
+) -> Tuple[Union[Chain, VectorStore], Dict[str, Any]]:
     langchain_object, artifacts = get_build_result(data_graph, session_id)
     session_id = build_sorted_vertices_with_caching.hash
-
     logger.debug("Loaded LangChain object")
-    if inputs is None:
-        inputs = {}
-
-    # Add artifacts to inputs
-    # artifacts can be documents loaded when building
-    # the flow
-    for (
-        key,
-        value,
-    ) in artifacts.items():
-        if key not in inputs or not inputs[key]:
-            inputs[key] = value
 
     if langchain_object is None:
-        # Raise user facing error
         raise ValueError(
             "There was an error loading the langchain_object. Please, check all the nodes and try again."
         )
 
-    # Generate result and thought
+    return langchain_object, artifacts, session_id
+
+
+def process_inputs(inputs: Optional[dict], artifacts: Dict[str, Any]) -> dict:
+    if inputs is None:
+        inputs = {}
+
+    for key, value in artifacts.items():
+        if key not in inputs or not inputs[key]:
+            inputs[key] = value
+
+    return inputs
+
+
+def generate_result(langchain_object: Union[Chain, VectorStore], inputs: dict):
     if isinstance(langchain_object, Chain):
         if inputs is None:
             raise ValueError("Inputs must be provided for a Chain")
@@ -152,6 +146,25 @@ def process_graph_cached(
         raise ValueError(
             f"Unknown langchain_object type: {type(langchain_object).__name__}"
         )
+
+    return result
+
+
+def process_graph_cached(
+    data_graph: Dict[str, Any],
+    inputs: Optional[dict] = None,
+    clear_cache=False,
+    session_id=None,
+) -> Tuple[Any, str]:
+    clear_caches_if_needed(clear_cache)
+    # If session_id is provided, load the langchain_object from the session
+    # else build the graph and return the result and the new session_id
+    langchain_object, artifacts, session_id = load_langchain_object(
+        data_graph, session_id
+    )
+    processed_inputs = process_inputs(inputs, artifacts)
+    result = generate_result(langchain_object, processed_inputs)
+
     return result, session_id