Merge branch 'main' into vertex_ai

src/backend/langflow/config.yaml

@@ -123,7 +123,7 @@ llms:
     documentation: "https://python.langchain.com/docs/modules/model_io/models/llms/integrations/google_vertex_ai_palm"
 memories:
   ConversationBufferMemory:
-    documentation: "https://python.langchain.com/docs/modules/memory/how_to/summary"
+    documentation: "https://python.langchain.com/docs/modules/memory/how_to/buffer"
   ConversationSummaryMemory:
     documentation: "https://python.langchain.com/docs/modules/memory/how_to/summary"
   ConversationKGMemory:

src/backend/langflow/interface/initialize/loading.py

@@ -188,18 +188,22 @@ def instantiate_documentloader(class_object: Type[BaseLoader], params: Dict):
             extension.strip() in x for extension in extensions
         )
     metadata = params.pop("metadata", None)
+    if metadata and isinstance(metadata, str):
+        try:
+            metadata = json.loads(metadata)
+        except json.JSONDecodeError as exc:
+            raise ValueError(
+                "The metadata you provided is not a valid JSON string."
+            ) from exc
     docs = class_object(**params).load()
+    # Now if metadata is an empty dict, we will not add it to the documents
     if metadata:
-        if isinstance(metadata, str):
-            try:
-                metadata = json.loads(metadata)
-            except json.JSONDecodeError as exc:
-                raise ValueError(
-                    "The metadata you provided is not a valid JSON string."
-                ) from exc
-
         for doc in docs:
-            doc.metadata = metadata
+            # If the document already has metadata, we will not overwrite it
+            if not doc.metadata:
+                doc.metadata = metadata
+            else:
+                doc.metadata.update(metadata)
 
     return docs
 
@@ -216,13 +220,19 @@ def instantiate_textsplitter(
             "Try changing the chunk_size of the Text Splitter."
         ) from exc
 
-    if "separator_type" in params and params["separator_type"] == "Text":
+    if (
+        "separator_type" in params and params["separator_type"] == "Text"
+    ) or "separator_type" not in params:
+        params.pop("separator_type", None)
         text_splitter = class_object(**params)
     else:
-        params["language"] = params.pop("separator_type", None)
-        params.pop("separators", None)
-        text_splitter = class_object.from_language(**params)
+        from langchain.text_splitter import Language
 
+        language = params.pop("separator_type", None)
+        params["language"] = Language(language)
+        params.pop("separators", None)
+
+        text_splitter = class_object.from_language(**params)
     return text_splitter.split_documents(documents)
 
 

src/backend/langflow/template/frontend_node/textsplitters.py

@@ -21,6 +21,8 @@ class TextSplittersFrontendNode(FrontendNode):
             # Add a field for type of separator
             # which will have Text or any value from the
             # Language enum
+            options = [x.value for x in Language] + ["Text"]
+            options.sort()
             self.template.add_field(
                 TemplateField(
                     field_type="str",
@@ -29,7 +31,7 @@ class TextSplittersFrontendNode(FrontendNode):
                     name="separator_type",
                     advanced=False,
                     is_list=True,
-                    options=[x.value for x in Language],
+                    options=options,
                     value="Text",
                     display_name="Separator Type",
                 )

src/frontend/src/pages/FlowPage/components/nodeToolbarComponent/index.tsx

@@ -81,6 +81,8 @@ const NodeToolbarComponent = (props) => {
                     ? " text-muted-foreground"
                     : " text-foreground")
               )}
+              target="_blank"
+              rel="noopener noreferrer"
               href={props.data.node.documentation}
               // deactivate link if no documentation is provided
               onClick={(event) => {