🔧 refactor(process.py): remove unused imports and variables, refactor function names for clarity

✨ feat(process.py): introduce SessionManager to handle loading and updating langchain_object sessions
🐛 fix(process.py): update cache with the updated langchain_object after processing graph

src/backend/langflow/processing/process.py

@@ -2,10 +2,11 @@ from pathlib import Path
 from langchain.schema import AgentAction
 import json
 from langflow.interface.run import (
-    build_sorted_vertices_with_caching,
+    build_sorted_vertices,
     get_memory_key,
     update_memory_keys,
 )
+from langflow.services.utils import get_session_manager
 from langflow.utils.logger import logger
 from langflow.graph import Graph
 from langchain.chains.base import Chain
@@ -92,18 +93,18 @@ def get_build_result(data_graph, session_id):
     # otherwise, build the graph and return the result
     if session_id:
         logger.debug(f"Loading LangChain object from session {session_id}")
-        result = build_sorted_vertices_with_caching.get_result_by_session_id(session_id)
+        result = build_sorted_vertices(data_graph=data_graph, session_id=session_id)
         if result is not None:
             logger.debug("Loaded LangChain object")
             return result
 
     logger.debug("Building langchain object")
-    return build_sorted_vertices_with_caching(data_graph)
+    return build_sorted_vertices(data_graph, session_id)
 
 
 def clear_caches_if_needed(clear_cache: bool):
     if clear_cache:
-        build_sorted_vertices_with_caching.clear_cache()
+        build_sorted_vertices.clear_cache()
         logger.debug("Cleared cache")
 
 
@@ -111,7 +112,6 @@ def load_langchain_object(
     data_graph: Dict[str, Any], session_id: str
 ) -> Tuple[Union[Chain, VectorStore], Dict[str, Any], str]:
     langchain_object, artifacts = get_build_result(data_graph, session_id)
-    session_id = build_sorted_vertices_with_caching.session_id
     logger.debug("Loaded LangChain object")
 
     if langchain_object is None:
@@ -139,6 +139,7 @@ def generate_result(langchain_object: Union[Chain, VectorStore], inputs: dict):
             raise ValueError("Inputs must be provided for a Chain")
         logger.debug("Generating result and thought")
         result = get_result_and_thought(langchain_object, inputs)
+
         logger.debug("Generated result and thought")
     elif isinstance(langchain_object, VectorStore):
         result = langchain_object.search(**inputs)
@@ -150,6 +151,28 @@ def generate_result(langchain_object: Union[Chain, VectorStore], inputs: dict):
     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)
+#     if result:
+#         # we need to update the cache with the updated langchain_object
+#         build_sorted_vertices_with_caching.update_cache(
+#             session_id, (langchain_object, artifacts)
+#         )
+#     return result, session_id
+
+
 def process_graph_cached(
     data_graph: Dict[str, Any],
     inputs: Optional[dict] = None,
@@ -157,13 +180,16 @@ def process_graph_cached(
     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
-    )
+    session_manager = get_session_manager()
+    # Load the graph using SessionManager
+    langchain_object, artifacts = session_manager.load_session(session_id, data_graph)
     processed_inputs = process_inputs(inputs, artifacts)
     result = generate_result(langchain_object, processed_inputs)
+    # langchain_object is now updated with the new memory
+    # we need to update the cache with the updated langchain_object
+    session_manager.update_session(
+        session_id, data_graph, (langchain_object, artifacts)
+    )
 
     return result, session_id