Merge remote-tracking branch 'origin/main' into dev

src/backend/langflow/api/chat_manager.py

@@ -110,7 +110,7 @@ class ChatManager:
         start_resp = ChatResponse(message=None, type="start", intermediate_steps="")
         await self.send_json(client_id, start_resp)
 
-        is_first_message = len(self.chat_history.get_history(client_id=client_id)) == 0
+        is_first_message = len(self.chat_history.get_history(client_id=client_id)) <= 1
         # Generate result and thought
         try:
             logger.debug("Generating result and thought")

src/backend/langflow/graph/nodes.py

@@ -36,7 +36,7 @@ class AgentNode(Node):
         #! Cannot deepcopy VectorStore, VectorStoreRouter, or SQL agents
         if self.node_type in ["VectorStoreAgent", "VectorStoreRouterAgent", "SQLAgent"]:
             return self._built_object
-        return deepcopy(self._built_object)
+        return self._built_object
 
 
 class ToolNode(Node):
@@ -81,7 +81,7 @@ class PromptNode(Node):
             self.params["input_variables"] = list(set(self.params["input_variables"]))
 
             self._build()
-        return deepcopy(self._built_object)
+        return self._built_object
 
 
 class ChainNode(Node):
@@ -105,13 +105,29 @@ class ChainNode(Node):
         #! Cannot deepcopy SQLDatabaseChain
         if self.node_type in ["SQLDatabaseChain"]:
             return self._built_object
-        return deepcopy(self._built_object)
+        return self._built_object
 
 
 class LLMNode(Node):
+    built_node_type = None
+    class_built_object = None
+
     def __init__(self, data: Dict):
         super().__init__(data, base_type="llms")
 
+    def build(self, force: bool = False) -> Any:
+        # LLM is different because some models might take up too much memory
+        # or time to load. So we only load them when we need them.ß
+        if self.node_type == self.built_node_type:
+            return self.class_built_object
+        if not self._built or force:
+            self._build()
+            self.built_node_type = self.node_type
+            self.class_built_object = self._built_object
+        # Avoid deepcopying the LLM
+        # that are loaded from a file
+        return self._built_object
+
 
 class ToolkitNode(Node):
     def __init__(self, data: Dict):
@@ -132,7 +148,7 @@ class WrapperNode(Node):
             if "headers" in self.params:
                 self.params["headers"] = eval(self.params["headers"])
             self._build()
-        return deepcopy(self._built_object)
+        return self._built_object
 
 
 class DocumentLoaderNode(Node):

src/backend/langflow/interface/loading.py

@@ -134,7 +134,13 @@ def instantiate_documentloader(class_object, params):
 
 
 def instantiate_textsplitter(class_object, params):
-    documents = params.pop("documents")
+    try:
+        documents = params.pop("documents")
+    except KeyError as e:
+        raise ValueError(
+            "The source you provided did not load correctly or was empty."
+            "Try changing the chunk_size of the Text Splitter."
+        ) from e
     text_splitter = class_object(**params)
     return text_splitter.split_documents(documents)
 

src/frontend/src/contexts/tabsContext.tsx

@@ -50,10 +50,23 @@ export function TabsProvider({ children }: { children: ReactNode }) {
 		return newNodeId.current;
 	}
 	function save() {
-		if (flows.length !== 0)
+		let Saveflows = [...flows];
+		if (Saveflows.length !== 0)
+		Saveflows.forEach((flow) => {
+			if(flow.data && flow.data?.nodes) flow.data?.nodes.forEach((node) => {
+				console.log(node.data.type)
+				Object.keys(node.data.node.template).forEach((key) => {
+					console.log(node.data.node.template[key].type)
+					if(node.data.node.template[key].type==="file"){
+						console.log(node.data.node.template[key])
+						node.data.node.template[key].content = "";
+					}
+				})
+			})
+		})
 			window.localStorage.setItem(
 				"tabsData",
-				JSON.stringify({ tabIndex, flows, id})
+				JSON.stringify({ tabIndex, flows:Saveflows, id})
 			);
 	}
 	useEffect(() => {