refactor: update Output definitions to remove incorrectly passed parameters

src/backend/base/langflow/components/apify/apify_actor.py

@@ -80,8 +80,8 @@ class ApifyActorsComponent(Component):
     ]
 
     outputs = [
-        Output(display_name="Output", name="output", type_=list[Data], method="run_model"),
-        Output(display_name="Tool", name="tool", type_=Tool, method="build_tool"),
+        Output(display_name="Output", name="output", method="run_model"),
+        Output(display_name="Tool", name="tool", method="build_tool"),
     ]
 
     def __init__(self, *args, **kwargs) -> None:

src/backend/base/langflow/components/helpers/batch_run.py

@@ -72,7 +72,6 @@ class BatchRunComponent(Component):
             display_name="DataFrame",
             name="batch_results",
             method="run_batch",
-            info="A DataFrame with all original columns plus the model's response column.",
         ),
     ]
 

src/backend/base/langflow/components/helpers/output_parser.py

@@ -26,7 +26,6 @@ class OutputParserComponent(Component):
         Output(
             display_name="Format Instructions",
             name="format_instructions",
-            info="Pass to a prompt template to include formatting instructions for LLM responses.",
             method="format_instructions",
         ),
         Output(display_name="Output Parser", name="output_parser", method="build_parser"),

src/backend/base/langflow/components/needle/needle.py

@@ -42,7 +42,7 @@ class NeedleComponent(Component):
         ),
     ]
 
-    outputs = [Output(display_name="Result", name="result", type_="Message", method="run")]
+    outputs = [Output(display_name="Result", name="result", method="run")]
 
     def run(self) -> Message:
         # Extract query and top_k

src/backend/base/langflow/components/processing/alter_metadata.py

@@ -46,13 +46,11 @@ class AlterMetadataComponent(Component):
         Output(
             name="data",
             display_name="Data",
-            info="List of Input objects each with added Metadata",
             method="process_output",
         ),
         Output(
             display_name="DataFrame",
             name="dataframe",
-            info="Data objects as a DataFrame, with metadata as columns",
             method="as_dataframe",
         ),
     ]

src/backend/base/langflow/components/processing/data_to_dataframe.py

@@ -27,7 +27,6 @@ class DataToDataFrameComponent(Component):
             display_name="DataFrame",
             name="dataframe",
             method="build_dataframe",
-            info="A DataFrame built from each Data object's fields plus a 'text' column.",
         ),
     ]
 

src/backend/base/langflow/components/processing/dataframe_operations.py

@@ -106,7 +106,6 @@ class DataFrameOperationsComponent(Component):
             display_name="DataFrame",
             name="output",
             method="perform_operation",
-            info="The resulting DataFrame after the operation.",
         )
     ]
 

src/backend/base/langflow/components/processing/parse_data.py

@@ -38,13 +38,11 @@ class ParseDataComponent(Component):
         Output(
             display_name="Message",
             name="text",
-            info="Data as a single Message, with each input Data separated by Separator",
             method="parse_data",
         ),
         Output(
             display_name="Data List",
             name="data_list",
-            info="Data as a list of new Data, each having `text` formatted by Template",
             method="parse_data_as_list",
         ),
     ]

src/backend/base/langflow/components/processing/parse_dataframe.py

@@ -37,7 +37,6 @@ class ParseDataFrameComponent(Component):
         Output(
             display_name="Text",
             name="text",
-            info="All rows combined into a single text, each row formatted by the template and separated by `sep`.",
             method="parse_data",
         ),
     ]

src/backend/base/langflow/components/processing/parser.py

@@ -64,7 +64,6 @@ class ParserComponent(Component):
         Output(
             display_name="Parsed Text",
             name="parsed_text",
-            info="Formatted text output.",
             method="parse_combined_text",
         ),
     ]

src/backend/base/langflow/components/processing/save_to_file.py

@@ -76,7 +76,6 @@ class SaveToFileComponent(Component):
             name="confirmation",
             display_name="Confirmation",
             method="save_to_file",
-            info="Confirmation message after saving the file.",
         ),
     ]
 

src/backend/base/langflow/components/tools/calculator_core.py

@@ -32,7 +32,7 @@ class CalculatorComponent(Component):
     ]
 
     outputs = [
-        Output(display_name="Data", name="result", type_=Data, method="evaluate_expression"),
+        Output(display_name="Data", name="result", method="evaluate_expression"),
     ]
 
     def _eval_expr(self, node: ast.AST) -> float:

src/backend/base/langflow/components/tools/google_search_api_core.py

@@ -38,7 +38,6 @@ class GoogleSearchAPICore(Component):
         Output(
             display_name="Results",
             name="results",
-            type_=DataFrame,
             method="search_google",
         ),
     ]

src/backend/base/langflow/components/tools/google_serper_api_core.py

@@ -34,7 +34,6 @@ class GoogleSerperAPICore(Component):
         Output(
             display_name="Results",
             name="results",
-            type_=DataFrame,
             method="search_serper",
         ),
     ]

src/backend/base/langflow/components/tools/python_repl_core.py

@@ -38,7 +38,6 @@ class PythonREPLComponent(Component):
         Output(
             display_name="Results",
             name="results",
-            type_=Data,
             method="run_python_repl",
         ),
     ]

src/backend/base/langflow/initial_setup/starter_projects/Invoice Summarizer.json

@@ -704,7 +704,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langchain_community.retrievers.needle import NeedleRetriever\n\nfrom langflow.custom.custom_component.component import Component\nfrom langflow.io import IntInput, MessageTextInput, Output, SecretStrInput\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI\n\n\nclass NeedleComponent(Component):\n    display_name = \"Needle Retriever\"\n    description = \"A retriever that uses the Needle API to search collections.\"\n    documentation = \"https://docs.needle-ai.com\"\n    icon = \"Needle\"\n    name = \"needle\"\n\n    inputs = [\n        SecretStrInput(\n            name=\"needle_api_key\",\n            display_name=\"Needle API Key\",\n            info=\"Your Needle API key.\",\n            required=True,\n        ),\n        MessageTextInput(\n            name=\"collection_id\",\n            display_name=\"Collection ID\",\n            info=\"The ID of the Needle collection.\",\n            required=True,\n        ),\n        MessageTextInput(\n            name=\"query\",\n            display_name=\"User Query\",\n            info=\"Enter your question here. In tool mode, you can also specify top_k parameter (min: 20).\",\n            required=True,\n            tool_mode=True,\n        ),\n        IntInput(\n            name=\"top_k\",\n            display_name=\"Top K Results\",\n            info=\"Number of search results to return (min: 20).\",\n            value=20,\n            required=True,\n        ),\n    ]\n\n    outputs = [Output(display_name=\"Result\", name=\"result\", type_=\"Message\", method=\"run\")]\n\n    def run(self) -> Message:\n        # Extract query and top_k\n        query_input = self.query\n        actual_query = query_input.get(\"query\", \"\") if isinstance(query_input, dict) else query_input\n\n        # Parse top_k from tool input or use default, always enforcing minimum of 20\n        try:\n            if isinstance(query_input, dict) and \"top_k\" in query_input:\n                agent_top_k = query_input.get(\"top_k\")\n                # Check if agent_top_k is not None before converting to int\n                top_k = max(20, int(agent_top_k)) if agent_top_k is not None else max(20, self.top_k)\n            else:\n                top_k = max(20, self.top_k)\n        except (ValueError, TypeError):\n            top_k = max(20, self.top_k)\n\n        # Validate required inputs\n        if not self.needle_api_key or not self.needle_api_key.strip():\n            error_msg = \"The Needle API key cannot be empty.\"\n            raise ValueError(error_msg)\n        if not self.collection_id or not self.collection_id.strip():\n            error_msg = \"The Collection ID cannot be empty.\"\n            raise ValueError(error_msg)\n        if not actual_query or not actual_query.strip():\n            error_msg = \"The query cannot be empty.\"\n            raise ValueError(error_msg)\n\n        try:\n            # Initialize the retriever and get documents\n            retriever = NeedleRetriever(\n                needle_api_key=self.needle_api_key,\n                collection_id=self.collection_id,\n                top_k=top_k,\n            )\n\n            docs = retriever.get_relevant_documents(actual_query)\n\n            # Format the response\n            if not docs:\n                text_content = \"No relevant documents found for the query.\"\n            else:\n                context = \"\\n\\n\".join([f\"Document {i + 1}:\\n{doc.page_content}\" for i, doc in enumerate(docs)])\n                text_content = f\"Question: {actual_query}\\n\\nContext:\\n{context}\"\n\n            # Return formatted message\n            return Message(\n                text=text_content,\n                type=\"assistant\",\n                sender=MESSAGE_SENDER_AI,\n                additional_kwargs={\n                    \"source_documents\": [{\"page_content\": doc.page_content, \"metadata\": doc.metadata} for doc in docs],\n                    \"top_k_used\": top_k,\n                },\n            )\n\n        except Exception as e:\n            error_msg = f\"Error processing query: {e!s}\"\n            raise ValueError(error_msg) from e\n"
+                "value": "from langchain_community.retrievers.needle import NeedleRetriever\n\nfrom langflow.custom.custom_component.component import Component\nfrom langflow.io import IntInput, MessageTextInput, Output, SecretStrInput\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI\n\n\nclass NeedleComponent(Component):\n    display_name = \"Needle Retriever\"\n    description = \"A retriever that uses the Needle API to search collections.\"\n    documentation = \"https://docs.needle-ai.com\"\n    icon = \"Needle\"\n    name = \"needle\"\n\n    inputs = [\n        SecretStrInput(\n            name=\"needle_api_key\",\n            display_name=\"Needle API Key\",\n            info=\"Your Needle API key.\",\n            required=True,\n        ),\n        MessageTextInput(\n            name=\"collection_id\",\n            display_name=\"Collection ID\",\n            info=\"The ID of the Needle collection.\",\n            required=True,\n        ),\n        MessageTextInput(\n            name=\"query\",\n            display_name=\"User Query\",\n            info=\"Enter your question here. In tool mode, you can also specify top_k parameter (min: 20).\",\n            required=True,\n            tool_mode=True,\n        ),\n        IntInput(\n            name=\"top_k\",\n            display_name=\"Top K Results\",\n            info=\"Number of search results to return (min: 20).\",\n            value=20,\n            required=True,\n        ),\n    ]\n\n    outputs = [Output(display_name=\"Result\", name=\"result\", method=\"run\")]\n\n    def run(self) -> Message:\n        # Extract query and top_k\n        query_input = self.query\n        actual_query = query_input.get(\"query\", \"\") if isinstance(query_input, dict) else query_input\n\n        # Parse top_k from tool input or use default, always enforcing minimum of 20\n        try:\n            if isinstance(query_input, dict) and \"top_k\" in query_input:\n                agent_top_k = query_input.get(\"top_k\")\n                # Check if agent_top_k is not None before converting to int\n                top_k = max(20, int(agent_top_k)) if agent_top_k is not None else max(20, self.top_k)\n            else:\n                top_k = max(20, self.top_k)\n        except (ValueError, TypeError):\n            top_k = max(20, self.top_k)\n\n        # Validate required inputs\n        if not self.needle_api_key or not self.needle_api_key.strip():\n            error_msg = \"The Needle API key cannot be empty.\"\n            raise ValueError(error_msg)\n        if not self.collection_id or not self.collection_id.strip():\n            error_msg = \"The Collection ID cannot be empty.\"\n            raise ValueError(error_msg)\n        if not actual_query or not actual_query.strip():\n            error_msg = \"The query cannot be empty.\"\n            raise ValueError(error_msg)\n\n        try:\n            # Initialize the retriever and get documents\n            retriever = NeedleRetriever(\n                needle_api_key=self.needle_api_key,\n                collection_id=self.collection_id,\n                top_k=top_k,\n            )\n\n            docs = retriever.get_relevant_documents(actual_query)\n\n            # Format the response\n            if not docs:\n                text_content = \"No relevant documents found for the query.\"\n            else:\n                context = \"\\n\\n\".join([f\"Document {i + 1}:\\n{doc.page_content}\" for i, doc in enumerate(docs)])\n                text_content = f\"Question: {actual_query}\\n\\nContext:\\n{context}\"\n\n            # Return formatted message\n            return Message(\n                text=text_content,\n                type=\"assistant\",\n                sender=MESSAGE_SENDER_AI,\n                additional_kwargs={\n                    \"source_documents\": [{\"page_content\": doc.page_content, \"metadata\": doc.metadata} for doc in docs],\n                    \"top_k_used\": top_k,\n                },\n            )\n\n        except Exception as e:\n            error_msg = f\"Error processing query: {e!s}\"\n            raise ValueError(error_msg) from e\n"
               },
               "collection_id": {
                 "_input_type": "MessageTextInput",

src/backend/base/langflow/initial_setup/starter_projects/LoopTemplate.json

@@ -1021,7 +1021,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text, data_to_text_list\nfrom langflow.io import DataInput, MultilineInput, Output, StrInput\nfrom langflow.schema import Data\nfrom langflow.schema.message import Message\n\n\nclass ParseDataComponent(Component):\n    display_name = \"Data to Message\"\n    description = \"Convert Data objects into Messages using any {field_name} from input data.\"\n    icon = \"message-square\"\n    name = \"ParseData\"\n    legacy = True\n    metadata = {\n        \"legacy_name\": \"Parse Data\",\n    }\n\n    inputs = [\n        DataInput(\n            name=\"data\",\n            display_name=\"Data\",\n            info=\"The data to convert to text.\",\n            is_list=True,\n            required=True,\n        ),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. \"\n            \"It can contain the keys {text}, {data} or any other key in the Data.\",\n            value=\"{text}\",\n            required=True,\n        ),\n        StrInput(name=\"sep\", display_name=\"Separator\", advanced=True, value=\"\\n\"),\n    ]\n\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"text\",\n            info=\"Data as a single Message, with each input Data separated by Separator\",\n            method=\"parse_data\",\n        ),\n        Output(\n            display_name=\"Data List\",\n            name=\"data_list\",\n            info=\"Data as a list of new Data, each having `text` formatted by Template\",\n            method=\"parse_data_as_list\",\n        ),\n    ]\n\n    def _clean_args(self) -> tuple[list[Data], str, str]:\n        data = self.data if isinstance(self.data, list) else [self.data]\n        template = self.template\n        sep = self.sep\n        return data, template, sep\n\n    def parse_data(self) -> Message:\n        data, template, sep = self._clean_args()\n        result_string = data_to_text(template, data, sep)\n        self.status = result_string\n        return Message(text=result_string)\n\n    def parse_data_as_list(self) -> list[Data]:\n        data, template, _ = self._clean_args()\n        text_list, data_list = data_to_text_list(template, data)\n        for item, text in zip(data_list, text_list, strict=True):\n            item.set_text(text)\n        self.status = data_list\n        return data_list\n"
+                "value": "from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text, data_to_text_list\nfrom langflow.io import DataInput, MultilineInput, Output, StrInput\nfrom langflow.schema import Data\nfrom langflow.schema.message import Message\n\n\nclass ParseDataComponent(Component):\n    display_name = \"Data to Message\"\n    description = \"Convert Data objects into Messages using any {field_name} from input data.\"\n    icon = \"message-square\"\n    name = \"ParseData\"\n    legacy = True\n    metadata = {\n        \"legacy_name\": \"Parse Data\",\n    }\n\n    inputs = [\n        DataInput(\n            name=\"data\",\n            display_name=\"Data\",\n            info=\"The data to convert to text.\",\n            is_list=True,\n            required=True,\n        ),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. \"\n            \"It can contain the keys {text}, {data} or any other key in the Data.\",\n            value=\"{text}\",\n            required=True,\n        ),\n        StrInput(name=\"sep\", display_name=\"Separator\", advanced=True, value=\"\\n\"),\n    ]\n\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"text\",\n            method=\"parse_data\",\n        ),\n        Output(\n            display_name=\"Data List\",\n            name=\"data_list\",\n            method=\"parse_data_as_list\",\n        ),\n    ]\n\n    def _clean_args(self) -> tuple[list[Data], str, str]:\n        data = self.data if isinstance(self.data, list) else [self.data]\n        template = self.template\n        sep = self.sep\n        return data, template, sep\n\n    def parse_data(self) -> Message:\n        data, template, sep = self._clean_args()\n        result_string = data_to_text(template, data, sep)\n        self.status = result_string\n        return Message(text=result_string)\n\n    def parse_data_as_list(self) -> list[Data]:\n        data, template, _ = self._clean_args()\n        text_list, data_list = data_to_text_list(template, data)\n        for item, text in zip(data_list, text_list, strict=True):\n            item.set_text(text)\n        self.status = data_list\n        return data_list\n"
               },
               "data": {
                 "_input_type": "DataInput",