fix: fixes for memory and better error message

src/backend/langflow/interface/run.py

@@ -55,7 +55,7 @@ def process_graph(data_graph: Dict[str, Any]):
     if langchain_object is None:
         # Raise user facing error
         raise ValueError(
-            "There was an error loading the flow. Please, check all the nodes and try again."
+            "There was an error loading the langchain_object. Please, check all the nodes and try again."
         )
 
     # Generate result and thought
@@ -72,47 +72,69 @@ def process_graph(data_graph: Dict[str, Any]):
     return {"result": str(result), "thought": thought.strip()}
 
 
-def get_result_and_thought_using_graph(loaded_langchain, message: str):
+def fix_memory_inputs_for_intermediate_steps(langchain_object):
+    """
+    Fix memory inputs by replacing the memory key with the input key.
+    """
+    langchain_object.return_intermediate_steps = True
+    langchain_object.memory.memory_key
+    input_key = [
+        key
+        for key in langchain_object.input_keys
+        if key != langchain_object.memory.memory_key
+    ][0]
+    # get output_key
+    output_key = [
+        key
+        for key in langchain_object.output_keys
+        if key != langchain_object.memory.memory_key
+    ][0]
+    # set input_key and output_key in memory
+    langchain_object.memory.input_key = input_key
+    langchain_object.memory.output_key = output_key
+
+
+def get_result_and_thought_using_graph(langchain_object, message: str):
     """Get result and thought from extracted json"""
     try:
-        if hasattr(loaded_langchain, "verbose"):
-            loaded_langchain.verbose = True
+        if hasattr(langchain_object, "verbose"):
+            langchain_object.verbose = True
         with io.StringIO() as output_buffer, contextlib.redirect_stdout(output_buffer):
             chat_input = None
             memory_key = ""
-            if hasattr(loaded_langchain, "memory"):
-                mem_vars = loaded_langchain.memory.memory_variables
-                memory_key = mem_vars[0] if mem_vars else ""
+            if (
+                hasattr(langchain_object, "memory")
+                and langchain_object.memory is not None
+            ):
+                memory_key = langchain_object.memory.memory_key
 
-            for key in loaded_langchain.input_keys:
-                if key != memory_key:
+            for key in langchain_object.input_keys:
+                if key not in [memory_key, "chat_history"]:
                     chat_input = {key: message}
 
-            if hasattr(loaded_langchain, "return_intermediate_steps"):
+            if hasattr(langchain_object, "return_intermediate_steps"):
                 # https://github.com/hwchase17/langchain/issues/2068
-                loaded_langchain.return_intermediate_steps = False
-
-            # I'm not sure about this yet.
-            function_to_call = None
-            if hasattr(loaded_langchain, "memory"):
-                function_to_call = loaded_langchain.predict
-            elif hasattr(loaded_langchain, "run"):
-                function_to_call = loaded_langchain.run
-            else:
-                function_to_call = loaded_langchain
+                # Deactivating until we have a frontend solution
+                # to display intermediate steps
+                langchain_object.return_intermediate_steps = False
+                if langchain_object.return_intermediate_steps:
+                    fix_memory_inputs_for_intermediate_steps(langchain_object)
 
             try:
-                output = function_to_call(chat_input)
+                output = langchain_object(chat_input)
             except ValueError as exc:
-                logger.debug("Error: %s", str(exc))
-                output = loaded_langchain.run(chat_input)
+                # make the error message more informative
+                logger.debug(f"Error: {str(exc)}")
+                if hasattr(langchain_object, "memory"):
+                    langchain_object.memory.memory_key = memory_key
+                output = langchain_object(chat_input)
 
             intermediate_steps = (
                 output.get("intermediate_steps", []) if isinstance(output, dict) else []
             )
 
             result = (
-                output.get(loaded_langchain.output_keys[0])
+                output.get(langchain_object.output_keys[0])
                 if isinstance(output, dict)
                 else output
             )
@@ -129,16 +151,16 @@ def get_result_and_thought_using_graph(loaded_langchain, message: str):
 def get_result_and_thought(extracted_json: Dict[str, Any], message: str):
     """Get result and thought from extracted json"""
     try:
-        loaded_langchain = loading.load_langchain_type_from_config(
+        langchain_object = loading.load_langchain_type_from_config(
             config=extracted_json
         )
         with io.StringIO() as output_buffer, contextlib.redirect_stdout(output_buffer):
-            output = loaded_langchain(message)
+            output = langchain_object(message)
             intermediate_steps = (
                 output.get("intermediate_steps", []) if isinstance(output, dict) else []
             )
             result = (
-                output.get(loaded_langchain.output_keys[0])
+                output.get(langchain_object.output_keys[0])
                 if isinstance(output, dict)
                 else output
             )