feat: change Text to Message type with backend and frontend support (#5652)

* ✨ (inputs.py): Update default prompt input types to include "MessageTextInput" for improved user experience
📝 (styleUtils.ts): Add color definition for "MessageTextInput" node to enhance visual representation in the frontend

* 📝 (text.py): Update input_types for "Value" to include "Message" for better clarity
📝 (memory.py): Update input_types for "Session ID" to include "Message" for consistency
📝 (message.py): Update input_types for "Session ID" to include "Message" for uniformity
📝 (self_query.py): Update input_types for "Query" to only include "Message" for consistency
📝 (create_data.py): Update input_types for fields to only include "Message" for consistency
📝 (update_data.py): Update input_types for fields to only include "Message" for consistency
📝 (inputs.py): Update DEFAULT_PROMPT_INTUT_TYPES to only include "Message" for consistency
📝 (styleUtils.ts): Remove "MessageTextInput" from nodeColors and nodeColorsName for consistency

* 📝 (model.py): update display name from "Text" to "Message" for better clarity
📝 (url.py): update display name from "Text" to "Message" for better consistency
📝 (memory.py): update display name from "Text" to "Message" for better understanding
📝 (text.py): update display name from "Text" to "Message" for better semantics
📝 (llm_math.py): update display name from "Text" to "Message" for improved readability
📝 (runnable_executor.py): update display name from "Text" to "Message" for better context
📝 (sql_generator.py): update display name from "Text" to "Message" for clearer communication
📝 (text.py): update display name from "Text" to "Message" for better consistency
📝 (parse_data.py): update display name from "Text" to "Message" for enhanced understanding
📝 (wikidata_api.py): update display name from "Text" to "Message" for improved semantics
📝 (test_cycles.py): update display name from "Text" to "Message" for better clarity

* [autofix.ci] apply automated fixes

* 🔧 (App.css): change width property value to fit-content to improve layout responsiveness

* fix: resolve merge conflicts and clean up URLComponent implementation in starter projects

- Removed conflicting sections in the JSON files for 'Custom Component Maker' and 'Graph Vector Store RAG'.
- Ensured the URLComponent class is correctly defined with methods for URL validation and content fetching.
- Updated input and output definitions for better clarity and functionality.

* 🐛 (freeze.spec.ts): fix incorrect test selectors for handle-textinput-shownode and handle-parsedata-shownode elements to match updated element IDs

* 📝 (NodeOutputfield/index.tsx): Add id prop to InspectButton component for better identification
🔧 (freeze-path.spec.ts, freeze.spec.ts, stop-building.spec.ts, decisionFlow.spec.ts, similarity.spec.ts, textInputOutput.spec.ts, generalBugs-shard-5.spec.ts, fileUploadComponent.spec.ts): Update test selectors to match changes in UI components for better test accuracy

* ✨ (duckduckgo.spec.ts): update the test to click on a specific element with the test ID "output-inspection-data-duckduckgosearch" instead of "output-inspection-data" to match the updated frontend implementation.

* ✨ (youtube-transcripts.spec.ts): update selector for clicking on transcript element to match changes in the frontend code, ensuring the test remains accurate

---------

Co-authored-by: Gabriel Luiz Freitas Almeida <gabriel@langflow.org>
Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>

src/backend/base/langflow/base/io/text.py

@@ -9,7 +9,7 @@ class TextComponent(Component):
         return {
             "input_value": {
                 "display_name": "Value",
-                "input_types": ["Text", "Data"],
+                "input_types": ["Message", "Data"],
                 "info": "Text or Data to be passed.",
             },
             "data_template": {

src/backend/base/langflow/base/memory/memory.py

@@ -23,7 +23,7 @@ class BaseMemoryComponent(CustomComponent):
             "session_id": {
                 "display_name": "Session ID",
                 "info": "Session ID of the chat history.",
-                "input_types": ["Text"],
+                "input_types": ["Message"],
             },
             "order": {
                 "options": ["Ascending", "Descending"],

src/backend/base/langflow/base/models/model.py

@@ -37,7 +37,7 @@ class LCModelComponent(Component):
     ]
 
     outputs = [
-        Output(display_name="Text", name="text_output", method="text_response"),
+        Output(display_name="Message", name="text_output", method="text_response"),
         Output(display_name="Language Model", name="model_output", method="build_model"),
     ]
 

src/backend/base/langflow/components/data/url.py

@@ -36,7 +36,7 @@ class URLComponent(Component):
 
     outputs = [
         Output(display_name="Data", name="data", method="fetch_content"),
-        Output(display_name="Text", name="text", method="fetch_content_text"),
+        Output(display_name="Message", name="text", method="fetch_content_text"),
         Output(display_name="DataFrame", name="dataframe", method="as_dataframe"),
     ]
 

src/backend/base/langflow/components/deactivated/message.py

@@ -19,7 +19,7 @@ class MessageComponent(CustomComponent):
             "session_id": {
                 "display_name": "Session ID",
                 "info": "Session ID of the chat history.",
-                "input_types": ["Text"],
+                "input_types": ["Message"],
             },
         }
 

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

@@ -69,7 +69,7 @@ class MemoryComponent(Component):
 
     outputs = [
         Output(display_name="Data", name="messages", method="retrieve_messages"),
-        Output(display_name="Text", name="messages_text", method="retrieve_messages_as_text"),
+        Output(display_name="Message", name="messages_text", method="retrieve_messages_as_text"),
     ]
 
     async def retrieve_messages(self) -> Data:

src/backend/base/langflow/components/inputs/text.py

@@ -17,7 +17,7 @@ class TextInputComponent(TextComponent):
         ),
     ]
     outputs = [
-        Output(display_name="Text", name="text", method="text_response"),
+        Output(display_name="Message", name="text", method="text_response"),
     ]
 
     def text_response(self) -> Message:

src/backend/base/langflow/components/langchain_utilities/llm_math.py

@@ -28,7 +28,7 @@ class LLMMathChainComponent(LCChainComponent):
         ),
     ]
 
-    outputs = [Output(display_name="Text", name="text", method="invoke_chain")]
+    outputs = [Output(display_name="Message", name="text", method="invoke_chain")]
 
     def invoke_chain(self) -> Message:
         chain = LLMMathChain.from_llm(llm=self.llm)

src/backend/base/langflow/components/langchain_utilities/runnable_executor.py

@@ -42,7 +42,7 @@ class RunnableExecComponent(Component):
 
     outputs = [
         Output(
-            display_name="Text",
+            display_name="Message",
             name="text",
             method="build_executor",
         ),

src/backend/base/langflow/components/langchain_utilities/self_query.py

@@ -20,7 +20,7 @@ class SelfQueryRetrieverComponent(Component):
             name="query",
             display_name="Query",
             info="Query to be passed as input.",
-            input_types=["Message", "Text"],
+            input_types=["Message"],
         ),
         HandleInput(
             name="vectorstore",

src/backend/base/langflow/components/langchain_utilities/sql_generator.py

@@ -51,7 +51,7 @@ class SQLGeneratorComponent(LCChainComponent):
         ),
     ]
 
-    outputs = [Output(display_name="Text", name="text", method="invoke_chain")]
+    outputs = [Output(display_name="Message", name="text", method="invoke_chain")]
 
     def invoke_chain(self) -> Message:
         prompt_template = PromptTemplate.from_template(template=self.prompt) if self.prompt else None

src/backend/base/langflow/components/outputs/text.py

@@ -17,7 +17,7 @@ class TextOutputComponent(TextComponent):
         ),
     ]
     outputs = [
-        Output(display_name="Text", name="text", method="text_response"),
+        Output(display_name="Message", name="text", method="text_response"),
     ]
 
     def text_response(self) -> Message:

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

@@ -74,7 +74,7 @@ class CreateDataComponent(Component):
                         display_name=f"Field {i}",
                         name=key,
                         info=f"Key for field {i}.",
-                        input_types=["Text", "Data"],
+                        input_types=["Message", "Data"],
                     )
                     build_config[field.name] = field.to_dict()
 

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

@@ -25,7 +25,7 @@ class ParseDataComponent(Component):
 
     outputs = [
         Output(
-            display_name="Text",
+            display_name="Message",
             name="text",
             info="Data as a single Message, with each input Data separated by Separator",
             method="parse_data",

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

@@ -97,7 +97,7 @@ class UpdateDataComponent(Component):
                         display_name=f"Field {i}",
                         name=key,
                         info=f"Key for field {i}.",
-                        input_types=["Text", "Data"],
+                        input_types=["Message", "Data"],
                     )
                     build_config[field.name] = field.to_dict()
 

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

@@ -27,7 +27,7 @@ class WikidataAPIComponent(Component):
 
     outputs = [
         Output(display_name="Data", name="data", method="fetch_content"),
-        Output(display_name="Text", name="text", method="fetch_content_text"),
+        Output(display_name="Message", name="text", method="fetch_content_text"),
     ]
 
     def fetch_content(self) -> list[Data]:

src/backend/base/langflow/initial_setup/starter_projects/Basic Prompt Chaining.json

@@ -1042,7 +1042,7 @@
             "outputs": [
               {
                 "cache": true,
-                "display_name": "Text",
+                "display_name": "Message",
                 "method": "text_response",
                 "name": "text_output",
                 "required_inputs": [],
@@ -1354,7 +1354,7 @@
             "outputs": [
               {
                 "cache": true,
-                "display_name": "Text",
+                "display_name": "Message",
                 "method": "text_response",
                 "name": "text_output",
                 "required_inputs": [],
@@ -1853,7 +1853,7 @@
             "outputs": [
               {
                 "cache": true,
-                "display_name": "Text",
+                "display_name": "Message",
                 "method": "text_response",
                 "name": "text_output",
                 "required_inputs": [],

src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting.json

@@ -876,7 +876,7 @@
             "outputs": [
               {
                 "cache": true,
-                "display_name": "Text",
+                "display_name": "Message",
                 "method": "text_response",
                 "name": "text_output",
                 "required_inputs": [],

src/backend/base/langflow/initial_setup/starter_projects/Blog Writer.json

@@ -179,7 +179,7 @@
               },
               {
                 "cache": true,
-                "display_name": "Text",
+                "display_name": "Message",
                 "method": "fetch_content_text",
                 "name": "text",
                 "selected": "Message",
@@ -219,7 +219,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import re\n\nfrom langchain_community.document_loaders import AsyncHtmlLoader, WebBaseLoader\n\nfrom langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\n\n\nclass URLComponent(Component):\n    display_name = \"URL\"\n    description = \"Load and retrive data from specified URLs.\"\n    icon = \"layout-template\"\n    name = \"URL\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"urls\",\n            display_name=\"URLs\",\n            is_list=True,\n            tool_mode=True,\n            placeholder=\"Enter a URL...\",\n            list_add_label=\"Add URL\",\n        ),\n        DropdownInput(\n            name=\"format\",\n            display_name=\"Output Format\",\n            info=\"Output Format. Use 'Text' to extract the text from the HTML or 'Raw HTML' for the raw HTML content.\",\n            options=[\"Text\", \"Raw HTML\"],\n            value=\"Text\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"data\", method=\"fetch_content\"),\n        Output(display_name=\"Text\", name=\"text\", method=\"fetch_content_text\"),\n        Output(display_name=\"DataFrame\", name=\"dataframe\", method=\"as_dataframe\"),\n    ]\n\n    def ensure_url(self, string: str) -> str:\n        \"\"\"Ensures the given string is a URL by adding 'http://' if it doesn't start with 'http://' or 'https://'.\n\n        Raises an error if the string is not a valid URL.\n\n        Parameters:\n            string (str): The string to be checked and possibly modified.\n\n        Returns:\n            str: The modified string that is ensured to be a URL.\n\n        Raises:\n            ValueError: If the string is not a valid URL.\n        \"\"\"\n        if not string.startswith((\"http://\", \"https://\")):\n            string = \"http://\" + string\n\n        # Basic URL validation regex\n        url_regex = re.compile(\n            r\"^(https?:\\/\\/)?\"  # optional protocol\n            r\"(www\\.)?\"  # optional www\n            r\"([a-zA-Z0-9.-]+)\"  # domain\n            r\"(\\.[a-zA-Z]{2,})?\"  # top-level domain\n            r\"(:\\d+)?\"  # optional port\n            r\"(\\/[^\\s]*)?$\",  # optional path\n            re.IGNORECASE,\n        )\n\n        if not url_regex.match(string):\n            msg = f\"Invalid URL: {string}\"\n            raise ValueError(msg)\n\n        return string\n\n    def fetch_content(self) -> list[Data]:\n        urls = [self.ensure_url(url.strip()) for url in self.urls if url.strip()]\n        if self.format == \"Raw HTML\":\n            loader = AsyncHtmlLoader(web_path=urls, encoding=\"utf-8\")\n        else:\n            loader = WebBaseLoader(web_paths=urls, encoding=\"utf-8\")\n        docs = loader.load()\n        data = [Data(text=doc.page_content, **doc.metadata) for doc in docs]\n        self.status = data\n        return data\n\n    def fetch_content_text(self) -> Message:\n        data = self.fetch_content()\n\n        result_string = data_to_text(\"{text}\", data)\n        self.status = result_string\n        return Message(text=result_string)\n\n    def as_dataframe(self) -> DataFrame:\n        return DataFrame(self.fetch_content())\n"
+                "value": "import re\n\nfrom langchain_community.document_loaders import AsyncHtmlLoader, WebBaseLoader\n\nfrom langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\n\n\nclass URLComponent(Component):\n    display_name = \"URL\"\n    description = \"Load and retrive data from specified URLs.\"\n    icon = \"layout-template\"\n    name = \"URL\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"urls\",\n            display_name=\"URLs\",\n            is_list=True,\n            tool_mode=True,\n            placeholder=\"Enter a URL...\",\n            list_add_label=\"Add URL\",\n        ),\n        DropdownInput(\n            name=\"format\",\n            display_name=\"Output Format\",\n            info=\"Output Format. Use 'Text' to extract the text from the HTML or 'Raw HTML' for the raw HTML content.\",\n            options=[\"Text\", \"Raw HTML\"],\n            value=\"Text\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"data\", method=\"fetch_content\"),\n        Output(display_name=\"Message\", name=\"text\", method=\"fetch_content_text\"),\n        Output(display_name=\"DataFrame\", name=\"dataframe\", method=\"as_dataframe\"),\n    ]\n\n    def ensure_url(self, string: str) -> str:\n        \"\"\"Ensures the given string is a URL by adding 'http://' if it doesn't start with 'http://' or 'https://'.\n\n        Raises an error if the string is not a valid URL.\n\n        Parameters:\n            string (str): The string to be checked and possibly modified.\n\n        Returns:\n            str: The modified string that is ensured to be a URL.\n\n        Raises:\n            ValueError: If the string is not a valid URL.\n        \"\"\"\n        if not string.startswith((\"http://\", \"https://\")):\n            string = \"http://\" + string\n\n        # Basic URL validation regex\n        url_regex = re.compile(\n            r\"^(https?:\\/\\/)?\"  # optional protocol\n            r\"(www\\.)?\"  # optional www\n            r\"([a-zA-Z0-9.-]+)\"  # domain\n            r\"(\\.[a-zA-Z]{2,})?\"  # top-level domain\n            r\"(:\\d+)?\"  # optional port\n            r\"(\\/[^\\s]*)?$\",  # optional path\n            re.IGNORECASE,\n        )\n\n        if not url_regex.match(string):\n            msg = f\"Invalid URL: {string}\"\n            raise ValueError(msg)\n\n        return string\n\n    def fetch_content(self) -> list[Data]:\n        urls = [self.ensure_url(url.strip()) for url in self.urls if url.strip()]\n        if self.format == \"Raw HTML\":\n            loader = AsyncHtmlLoader(web_path=urls, encoding=\"utf-8\")\n        else:\n            loader = WebBaseLoader(web_paths=urls, encoding=\"utf-8\")\n        docs = loader.load()\n        data = [Data(text=doc.page_content, **doc.metadata) for doc in docs]\n        self.status = data\n        return data\n\n    def fetch_content_text(self) -> Message:\n        data = self.fetch_content()\n\n        result_string = data_to_text(\"{text}\", data)\n        self.status = result_string\n        return Message(text=result_string)\n\n    def as_dataframe(self) -> DataFrame:\n        return DataFrame(self.fetch_content())\n"
               },
               "format": {
                 "_input_type": "DropdownInput",
@@ -313,7 +313,7 @@
             "outputs": [
               {
                 "cache": true,
-                "display_name": "Text",
+                "display_name": "Message",
                 "method": "parse_data",
                 "name": "text",
                 "selected": "Message",
@@ -353,7 +353,7 @@
                 "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 = \"Parse Data\"\n    description = \"Convert Data into plain text following a specified template.\"\n    icon = \"braces\"\n    name = \"ParseData\"\n\n    inputs = [\n        DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\", is_list=True),\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        ),\n        StrInput(name=\"sep\", display_name=\"Separator\", advanced=True, value=\"\\n\"),\n    ]\n\n    outputs = [\n        Output(\n            display_name=\"Text\",\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 = \"Parse Data\"\n    description = \"Convert Data into plain text following a specified template.\"\n    icon = \"braces\"\n    name = \"ParseData\"\n\n    inputs = [\n        DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\", is_list=True),\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        ),\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"
               },
               "data": {
                 "advanced": false,
@@ -627,7 +627,7 @@
             "outputs": [
               {
                 "cache": true,
-                "display_name": "Text",
+                "display_name": "Message",
                 "method": "text_response",
                 "name": "text",
                 "selected": "Message",
@@ -656,7 +656,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.text import TextComponent\nfrom langflow.io import MultilineInput, Output\nfrom langflow.schema.message import Message\n\n\nclass TextInputComponent(TextComponent):\n    display_name = \"Text Input\"\n    description = \"Get text inputs from the Playground.\"\n    icon = \"type\"\n    name = \"TextInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Text to be passed as input.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"text_response\"),\n    ]\n\n    def text_response(self) -> Message:\n        return Message(\n            text=self.input_value,\n        )\n"
+                "value": "from langflow.base.io.text import TextComponent\nfrom langflow.io import MultilineInput, Output\nfrom langflow.schema.message import Message\n\n\nclass TextInputComponent(TextComponent):\n    display_name = \"Text Input\"\n    description = \"Get text inputs from the Playground.\"\n    icon = \"type\"\n    name = \"TextInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Text to be passed as input.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"text\", method=\"text_response\"),\n    ]\n\n    def text_response(self) -> Message:\n        return Message(\n            text=self.input_value,\n        )\n"
               },
               "input_value": {
                 "_input_type": "MultilineInput",
@@ -999,7 +999,7 @@
             "outputs": [
               {
                 "cache": true,
-                "display_name": "Text",
+                "display_name": "Message",
                 "method": "text_response",
                 "name": "text_output",
                 "required_inputs": [],

src/backend/base/langflow/initial_setup/starter_projects/Custom Component Maker.json

@@ -542,7 +542,7 @@
               },
               {
                 "cache": true,
-                "display_name": "Text",
+                "display_name": "Message",
                 "method": "retrieve_messages_as_text",
                 "name": "messages_text",
                 "selected": "Message",
@@ -571,7 +571,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.inputs import HandleInput\nfrom langflow.io import DropdownInput, IntInput, MessageTextInput, MultilineInput, Output\nfrom langflow.memory import aget_messages\nfrom langflow.schema import Data\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_USER\n\n\nclass MemoryComponent(Component):\n    display_name = \"Message History\"\n    description = \"Retrieves stored chat messages from Langflow tables or an external memory.\"\n    icon = \"message-square-more\"\n    name = \"Memory\"\n\n    inputs = [\n        HandleInput(\n            name=\"memory\",\n            display_name=\"External Memory\",\n            input_types=[\"Memory\"],\n            info=\"Retrieve messages from an external memory. If empty, it will use the Langflow tables.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER, \"Machine and User\"],\n            value=\"Machine and User\",\n            info=\"Filter by sender type.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Filter by sender name.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Messages\",\n            value=100,\n            info=\"Number of messages to retrieve.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"order\",\n            display_name=\"Order\",\n            options=[\"Ascending\", \"Descending\"],\n            value=\"Ascending\",\n            info=\"Order of the messages.\",\n            advanced=True,\n            tool_mode=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}, {sender} or any other key in the message data.\",\n            value=\"{sender_name}: {text}\",\n            advanced=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"messages\", method=\"retrieve_messages\"),\n        Output(display_name=\"Text\", name=\"messages_text\", method=\"retrieve_messages_as_text\"),\n    ]\n\n    async def retrieve_messages(self) -> Data:\n        sender = self.sender\n        sender_name = self.sender_name\n        session_id = self.session_id\n        n_messages = self.n_messages\n        order = \"DESC\" if self.order == \"Descending\" else \"ASC\"\n\n        if sender == \"Machine and User\":\n            sender = None\n\n        if self.memory:\n            # override session_id\n            self.memory.session_id = session_id\n\n            stored = await self.memory.aget_messages()\n            # langchain memories are supposed to return messages in ascending order\n            if order == \"DESC\":\n                stored = stored[::-1]\n            if n_messages:\n                stored = stored[:n_messages]\n            stored = [Message.from_lc_message(m) for m in stored]\n            if sender:\n                expected_type = MESSAGE_SENDER_AI if sender == MESSAGE_SENDER_AI else MESSAGE_SENDER_USER\n                stored = [m for m in stored if m.type == expected_type]\n        else:\n            stored = await aget_messages(\n                sender=sender,\n                sender_name=sender_name,\n                session_id=session_id,\n                limit=n_messages,\n                order=order,\n            )\n        self.status = stored\n        return stored\n\n    async def retrieve_messages_as_text(self) -> Message:\n        stored_text = data_to_text(self.template, await self.retrieve_messages())\n        self.status = stored_text\n        return Message(text=stored_text)\n"
+                "value": "from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.inputs import HandleInput\nfrom langflow.io import DropdownInput, IntInput, MessageTextInput, MultilineInput, Output\nfrom langflow.memory import aget_messages\nfrom langflow.schema import Data\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_USER\n\n\nclass MemoryComponent(Component):\n    display_name = \"Message History\"\n    description = \"Retrieves stored chat messages from Langflow tables or an external memory.\"\n    icon = \"message-square-more\"\n    name = \"Memory\"\n\n    inputs = [\n        HandleInput(\n            name=\"memory\",\n            display_name=\"External Memory\",\n            input_types=[\"Memory\"],\n            info=\"Retrieve messages from an external memory. If empty, it will use the Langflow tables.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER, \"Machine and User\"],\n            value=\"Machine and User\",\n            info=\"Filter by sender type.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Filter by sender name.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Messages\",\n            value=100,\n            info=\"Number of messages to retrieve.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"order\",\n            display_name=\"Order\",\n            options=[\"Ascending\", \"Descending\"],\n            value=\"Ascending\",\n            info=\"Order of the messages.\",\n            advanced=True,\n            tool_mode=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}, {sender} or any other key in the message data.\",\n            value=\"{sender_name}: {text}\",\n            advanced=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"messages\", method=\"retrieve_messages\"),\n        Output(display_name=\"Message\", name=\"messages_text\", method=\"retrieve_messages_as_text\"),\n    ]\n\n    async def retrieve_messages(self) -> Data:\n        sender = self.sender\n        sender_name = self.sender_name\n        session_id = self.session_id\n        n_messages = self.n_messages\n        order = \"DESC\" if self.order == \"Descending\" else \"ASC\"\n\n        if sender == \"Machine and User\":\n            sender = None\n\n        if self.memory:\n            # override session_id\n            self.memory.session_id = session_id\n\n            stored = await self.memory.aget_messages()\n            # langchain memories are supposed to return messages in ascending order\n            if order == \"DESC\":\n                stored = stored[::-1]\n            if n_messages:\n                stored = stored[:n_messages]\n            stored = [Message.from_lc_message(m) for m in stored]\n            if sender:\n                expected_type = MESSAGE_SENDER_AI if sender == MESSAGE_SENDER_AI else MESSAGE_SENDER_USER\n                stored = [m for m in stored if m.type == expected_type]\n        else:\n            stored = await aget_messages(\n                sender=sender,\n                sender_name=sender_name,\n                session_id=session_id,\n                limit=n_messages,\n                order=order,\n            )\n        self.status = stored\n        return stored\n\n    async def retrieve_messages_as_text(self) -> Message:\n        stored_text = data_to_text(self.template, await self.retrieve_messages())\n        self.status = stored_text\n        return Message(text=stored_text)\n"
               },
               "memory": {
                 "_input_type": "HandleInput",
@@ -1349,7 +1349,7 @@
             "outputs": [
               {
                 "cache": true,
-                "display_name": "Text",
+                "display_name": "Message",
                 "method": "text_response",
                 "name": "text_output",
                 "required_inputs": [],
@@ -1646,7 +1646,7 @@
               },
               {
                 "cache": true,
-                "display_name": "Text",
+                "display_name": "Message",
                 "method": "fetch_content_text",
                 "name": "text",
                 "selected": "Message",
@@ -1686,7 +1686,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import re\n\nfrom langchain_community.document_loaders import AsyncHtmlLoader, WebBaseLoader\n\nfrom langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\n\n\nclass URLComponent(Component):\n    display_name = \"URL\"\n    description = \"Load and retrive data from specified URLs.\"\n    icon = \"layout-template\"\n    name = \"URL\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"urls\",\n            display_name=\"URLs\",\n            is_list=True,\n            tool_mode=True,\n            placeholder=\"Enter a URL...\",\n            list_add_label=\"Add URL\",\n        ),\n        DropdownInput(\n            name=\"format\",\n            display_name=\"Output Format\",\n            info=\"Output Format. Use 'Text' to extract the text from the HTML or 'Raw HTML' for the raw HTML content.\",\n            options=[\"Text\", \"Raw HTML\"],\n            value=\"Text\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"data\", method=\"fetch_content\"),\n        Output(display_name=\"Text\", name=\"text\", method=\"fetch_content_text\"),\n        Output(display_name=\"DataFrame\", name=\"dataframe\", method=\"as_dataframe\"),\n    ]\n\n    def ensure_url(self, string: str) -> str:\n        \"\"\"Ensures the given string is a URL by adding 'http://' if it doesn't start with 'http://' or 'https://'.\n\n        Raises an error if the string is not a valid URL.\n\n        Parameters:\n            string (str): The string to be checked and possibly modified.\n\n        Returns:\n            str: The modified string that is ensured to be a URL.\n\n        Raises:\n            ValueError: If the string is not a valid URL.\n        \"\"\"\n        if not string.startswith((\"http://\", \"https://\")):\n            string = \"http://\" + string\n\n        # Basic URL validation regex\n        url_regex = re.compile(\n            r\"^(https?:\\/\\/)?\"  # optional protocol\n            r\"(www\\.)?\"  # optional www\n            r\"([a-zA-Z0-9.-]+)\"  # domain\n            r\"(\\.[a-zA-Z]{2,})?\"  # top-level domain\n            r\"(:\\d+)?\"  # optional port\n            r\"(\\/[^\\s]*)?$\",  # optional path\n            re.IGNORECASE,\n        )\n\n        if not url_regex.match(string):\n            msg = f\"Invalid URL: {string}\"\n            raise ValueError(msg)\n\n        return string\n\n    def fetch_content(self) -> list[Data]:\n        urls = [self.ensure_url(url.strip()) for url in self.urls if url.strip()]\n        if self.format == \"Raw HTML\":\n            loader = AsyncHtmlLoader(web_path=urls, encoding=\"utf-8\")\n        else:\n            loader = WebBaseLoader(web_paths=urls, encoding=\"utf-8\")\n        docs = loader.load()\n        data = [Data(text=doc.page_content, **doc.metadata) for doc in docs]\n        self.status = data\n        return data\n\n    def fetch_content_text(self) -> Message:\n        data = self.fetch_content()\n\n        result_string = data_to_text(\"{text}\", data)\n        self.status = result_string\n        return Message(text=result_string)\n\n    def as_dataframe(self) -> DataFrame:\n        return DataFrame(self.fetch_content())\n"
+                "value": "import re\n\nfrom langchain_community.document_loaders import AsyncHtmlLoader, WebBaseLoader\n\nfrom langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\n\n\nclass URLComponent(Component):\n    display_name = \"URL\"\n    description = \"Load and retrive data from specified URLs.\"\n    icon = \"layout-template\"\n    name = \"URL\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"urls\",\n            display_name=\"URLs\",\n            is_list=True,\n            tool_mode=True,\n            placeholder=\"Enter a URL...\",\n            list_add_label=\"Add URL\",\n        ),\n        DropdownInput(\n            name=\"format\",\n            display_name=\"Output Format\",\n            info=\"Output Format. Use 'Text' to extract the text from the HTML or 'Raw HTML' for the raw HTML content.\",\n            options=[\"Text\", \"Raw HTML\"],\n            value=\"Text\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"data\", method=\"fetch_content\"),\n        Output(display_name=\"Message\", name=\"text\", method=\"fetch_content_text\"),\n        Output(display_name=\"DataFrame\", name=\"dataframe\", method=\"as_dataframe\"),\n    ]\n\n    def ensure_url(self, string: str) -> str:\n        \"\"\"Ensures the given string is a URL by adding 'http://' if it doesn't start with 'http://' or 'https://'.\n\n        Raises an error if the string is not a valid URL.\n\n        Parameters:\n            string (str): The string to be checked and possibly modified.\n\n        Returns:\n            str: The modified string that is ensured to be a URL.\n\n        Raises:\n            ValueError: If the string is not a valid URL.\n        \"\"\"\n        if not string.startswith((\"http://\", \"https://\")):\n            string = \"http://\" + string\n\n        # Basic URL validation regex\n        url_regex = re.compile(\n            r\"^(https?:\\/\\/)?\"  # optional protocol\n            r\"(www\\.)?\"  # optional www\n            r\"([a-zA-Z0-9.-]+)\"  # domain\n            r\"(\\.[a-zA-Z]{2,})?\"  # top-level domain\n            r\"(:\\d+)?\"  # optional port\n            r\"(\\/[^\\s]*)?$\",  # optional path\n            re.IGNORECASE,\n        )\n\n        if not url_regex.match(string):\n            msg = f\"Invalid URL: {string}\"\n            raise ValueError(msg)\n\n        return string\n\n    def fetch_content(self) -> list[Data]:\n        urls = [self.ensure_url(url.strip()) for url in self.urls if url.strip()]\n        if self.format == \"Raw HTML\":\n            loader = AsyncHtmlLoader(web_path=urls, encoding=\"utf-8\")\n        else:\n            loader = WebBaseLoader(web_paths=urls, encoding=\"utf-8\")\n        docs = loader.load()\n        data = [Data(text=doc.page_content, **doc.metadata) for doc in docs]\n        self.status = data\n        return data\n\n    def fetch_content_text(self) -> Message:\n        data = self.fetch_content()\n\n        result_string = data_to_text(\"{text}\", data)\n        self.status = result_string\n        return Message(text=result_string)\n\n    def as_dataframe(self) -> DataFrame:\n        return DataFrame(self.fetch_content())\n"
               },
               "format": {
                 "_input_type": "DropdownInput",
@@ -1792,7 +1792,7 @@
               },
               {
                 "cache": true,
-                "display_name": "Text",
+                "display_name": "Message",
                 "method": "fetch_content_text",
                 "name": "text",
                 "selected": "Message",
@@ -1832,7 +1832,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import re\n\nfrom langchain_community.document_loaders import AsyncHtmlLoader, WebBaseLoader\n\nfrom langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\n\n\nclass URLComponent(Component):\n    display_name = \"URL\"\n    description = \"Load and retrive data from specified URLs.\"\n    icon = \"layout-template\"\n    name = \"URL\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"urls\",\n            display_name=\"URLs\",\n            is_list=True,\n            tool_mode=True,\n            placeholder=\"Enter a URL...\",\n            list_add_label=\"Add URL\",\n        ),\n        DropdownInput(\n            name=\"format\",\n            display_name=\"Output Format\",\n            info=\"Output Format. Use 'Text' to extract the text from the HTML or 'Raw HTML' for the raw HTML content.\",\n            options=[\"Text\", \"Raw HTML\"],\n            value=\"Text\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"data\", method=\"fetch_content\"),\n        Output(display_name=\"Text\", name=\"text\", method=\"fetch_content_text\"),\n        Output(display_name=\"DataFrame\", name=\"dataframe\", method=\"as_dataframe\"),\n    ]\n\n    def ensure_url(self, string: str) -> str:\n        \"\"\"Ensures the given string is a URL by adding 'http://' if it doesn't start with 'http://' or 'https://'.\n\n        Raises an error if the string is not a valid URL.\n\n        Parameters:\n            string (str): The string to be checked and possibly modified.\n\n        Returns:\n            str: The modified string that is ensured to be a URL.\n\n        Raises:\n            ValueError: If the string is not a valid URL.\n        \"\"\"\n        if not string.startswith((\"http://\", \"https://\")):\n            string = \"http://\" + string\n\n        # Basic URL validation regex\n        url_regex = re.compile(\n            r\"^(https?:\\/\\/)?\"  # optional protocol\n            r\"(www\\.)?\"  # optional www\n            r\"([a-zA-Z0-9.-]+)\"  # domain\n            r\"(\\.[a-zA-Z]{2,})?\"  # top-level domain\n            r\"(:\\d+)?\"  # optional port\n            r\"(\\/[^\\s]*)?$\",  # optional path\n            re.IGNORECASE,\n        )\n\n        if not url_regex.match(string):\n            msg = f\"Invalid URL: {string}\"\n            raise ValueError(msg)\n\n        return string\n\n    def fetch_content(self) -> list[Data]:\n        urls = [self.ensure_url(url.strip()) for url in self.urls if url.strip()]\n        if self.format == \"Raw HTML\":\n            loader = AsyncHtmlLoader(web_path=urls, encoding=\"utf-8\")\n        else:\n            loader = WebBaseLoader(web_paths=urls, encoding=\"utf-8\")\n        docs = loader.load()\n        data = [Data(text=doc.page_content, **doc.metadata) for doc in docs]\n        self.status = data\n        return data\n\n    def fetch_content_text(self) -> Message:\n        data = self.fetch_content()\n\n        result_string = data_to_text(\"{text}\", data)\n        self.status = result_string\n        return Message(text=result_string)\n\n    def as_dataframe(self) -> DataFrame:\n        return DataFrame(self.fetch_content())\n"
+                "value": "import re\n\nfrom langchain_community.document_loaders import AsyncHtmlLoader, WebBaseLoader\n\nfrom langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\n\n\nclass URLComponent(Component):\n    display_name = \"URL\"\n    description = \"Load and retrive data from specified URLs.\"\n    icon = \"layout-template\"\n    name = \"URL\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"urls\",\n            display_name=\"URLs\",\n            is_list=True,\n            tool_mode=True,\n            placeholder=\"Enter a URL...\",\n            list_add_label=\"Add URL\",\n        ),\n        DropdownInput(\n            name=\"format\",\n            display_name=\"Output Format\",\n            info=\"Output Format. Use 'Text' to extract the text from the HTML or 'Raw HTML' for the raw HTML content.\",\n            options=[\"Text\", \"Raw HTML\"],\n            value=\"Text\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"data\", method=\"fetch_content\"),\n        Output(display_name=\"Message\", name=\"text\", method=\"fetch_content_text\"),\n        Output(display_name=\"DataFrame\", name=\"dataframe\", method=\"as_dataframe\"),\n    ]\n\n    def ensure_url(self, string: str) -> str:\n        \"\"\"Ensures the given string is a URL by adding 'http://' if it doesn't start with 'http://' or 'https://'.\n\n        Raises an error if the string is not a valid URL.\n\n        Parameters:\n            string (str): The string to be checked and possibly modified.\n\n        Returns:\n            str: The modified string that is ensured to be a URL.\n\n        Raises:\n            ValueError: If the string is not a valid URL.\n        \"\"\"\n        if not string.startswith((\"http://\", \"https://\")):\n            string = \"http://\" + string\n\n        # Basic URL validation regex\n        url_regex = re.compile(\n            r\"^(https?:\\/\\/)?\"  # optional protocol\n            r\"(www\\.)?\"  # optional www\n            r\"([a-zA-Z0-9.-]+)\"  # domain\n            r\"(\\.[a-zA-Z]{2,})?\"  # top-level domain\n            r\"(:\\d+)?\"  # optional port\n            r\"(\\/[^\\s]*)?$\",  # optional path\n            re.IGNORECASE,\n        )\n\n        if not url_regex.match(string):\n            msg = f\"Invalid URL: {string}\"\n            raise ValueError(msg)\n\n        return string\n\n    def fetch_content(self) -> list[Data]:\n        urls = [self.ensure_url(url.strip()) for url in self.urls if url.strip()]\n        if self.format == \"Raw HTML\":\n            loader = AsyncHtmlLoader(web_path=urls, encoding=\"utf-8\")\n        else:\n            loader = WebBaseLoader(web_paths=urls, encoding=\"utf-8\")\n        docs = loader.load()\n        data = [Data(text=doc.page_content, **doc.metadata) for doc in docs]\n        self.status = data\n        return data\n\n    def fetch_content_text(self) -> Message:\n        data = self.fetch_content()\n\n        result_string = data_to_text(\"{text}\", data)\n        self.status = result_string\n        return Message(text=result_string)\n\n    def as_dataframe(self) -> DataFrame:\n        return DataFrame(self.fetch_content())\n"
               },
               "format": {
                 "_input_type": "DropdownInput",
@@ -1944,7 +1944,7 @@
               },
               {
                 "cache": true,
-                "display_name": "Text",
+                "display_name": "Message",
                 "method": "fetch_content_text",
                 "name": "text",
                 "selected": "Message",
@@ -1984,7 +1984,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import re\n\nfrom langchain_community.document_loaders import AsyncHtmlLoader, WebBaseLoader\n\nfrom langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\n\n\nclass URLComponent(Component):\n    display_name = \"URL\"\n    description = \"Load and retrive data from specified URLs.\"\n    icon = \"layout-template\"\n    name = \"URL\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"urls\",\n            display_name=\"URLs\",\n            is_list=True,\n            tool_mode=True,\n            placeholder=\"Enter a URL...\",\n            list_add_label=\"Add URL\",\n        ),\n        DropdownInput(\n            name=\"format\",\n            display_name=\"Output Format\",\n            info=\"Output Format. Use 'Text' to extract the text from the HTML or 'Raw HTML' for the raw HTML content.\",\n            options=[\"Text\", \"Raw HTML\"],\n            value=\"Text\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"data\", method=\"fetch_content\"),\n        Output(display_name=\"Text\", name=\"text\", method=\"fetch_content_text\"),\n        Output(display_name=\"DataFrame\", name=\"dataframe\", method=\"as_dataframe\"),\n    ]\n\n    def ensure_url(self, string: str) -> str:\n        \"\"\"Ensures the given string is a URL by adding 'http://' if it doesn't start with 'http://' or 'https://'.\n\n        Raises an error if the string is not a valid URL.\n\n        Parameters:\n            string (str): The string to be checked and possibly modified.\n\n        Returns:\n            str: The modified string that is ensured to be a URL.\n\n        Raises:\n            ValueError: If the string is not a valid URL.\n        \"\"\"\n        if not string.startswith((\"http://\", \"https://\")):\n            string = \"http://\" + string\n\n        # Basic URL validation regex\n        url_regex = re.compile(\n            r\"^(https?:\\/\\/)?\"  # optional protocol\n            r\"(www\\.)?\"  # optional www\n            r\"([a-zA-Z0-9.-]+)\"  # domain\n            r\"(\\.[a-zA-Z]{2,})?\"  # top-level domain\n            r\"(:\\d+)?\"  # optional port\n            r\"(\\/[^\\s]*)?$\",  # optional path\n            re.IGNORECASE,\n        )\n\n        if not url_regex.match(string):\n            msg = f\"Invalid URL: {string}\"\n            raise ValueError(msg)\n\n        return string\n\n    def fetch_content(self) -> list[Data]:\n        urls = [self.ensure_url(url.strip()) for url in self.urls if url.strip()]\n        if self.format == \"Raw HTML\":\n            loader = AsyncHtmlLoader(web_path=urls, encoding=\"utf-8\")\n        else:\n            loader = WebBaseLoader(web_paths=urls, encoding=\"utf-8\")\n        docs = loader.load()\n        data = [Data(text=doc.page_content, **doc.metadata) for doc in docs]\n        self.status = data\n        return data\n\n    def fetch_content_text(self) -> Message:\n        data = self.fetch_content()\n\n        result_string = data_to_text(\"{text}\", data)\n        self.status = result_string\n        return Message(text=result_string)\n\n    def as_dataframe(self) -> DataFrame:\n        return DataFrame(self.fetch_content())\n"
+                "value": "import re\n\nfrom langchain_community.document_loaders import AsyncHtmlLoader, WebBaseLoader\n\nfrom langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\n\n\nclass URLComponent(Component):\n    display_name = \"URL\"\n    description = \"Load and retrive data from specified URLs.\"\n    icon = \"layout-template\"\n    name = \"URL\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"urls\",\n            display_name=\"URLs\",\n            is_list=True,\n            tool_mode=True,\n            placeholder=\"Enter a URL...\",\n            list_add_label=\"Add URL\",\n        ),\n        DropdownInput(\n            name=\"format\",\n            display_name=\"Output Format\",\n            info=\"Output Format. Use 'Text' to extract the text from the HTML or 'Raw HTML' for the raw HTML content.\",\n            options=[\"Text\", \"Raw HTML\"],\n            value=\"Text\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"data\", method=\"fetch_content\"),\n        Output(display_name=\"Message\", name=\"text\", method=\"fetch_content_text\"),\n        Output(display_name=\"DataFrame\", name=\"dataframe\", method=\"as_dataframe\"),\n    ]\n\n    def ensure_url(self, string: str) -> str:\n        \"\"\"Ensures the given string is a URL by adding 'http://' if it doesn't start with 'http://' or 'https://'.\n\n        Raises an error if the string is not a valid URL.\n\n        Parameters:\n            string (str): The string to be checked and possibly modified.\n\n        Returns:\n            str: The modified string that is ensured to be a URL.\n\n        Raises:\n            ValueError: If the string is not a valid URL.\n        \"\"\"\n        if not string.startswith((\"http://\", \"https://\")):\n            string = \"http://\" + string\n\n        # Basic URL validation regex\n        url_regex = re.compile(\n            r\"^(https?:\\/\\/)?\"  # optional protocol\n            r\"(www\\.)?\"  # optional www\n            r\"([a-zA-Z0-9.-]+)\"  # domain\n            r\"(\\.[a-zA-Z]{2,})?\"  # top-level domain\n            r\"(:\\d+)?\"  # optional port\n            r\"(\\/[^\\s]*)?$\",  # optional path\n            re.IGNORECASE,\n        )\n\n        if not url_regex.match(string):\n            msg = f\"Invalid URL: {string}\"\n            raise ValueError(msg)\n\n        return string\n\n    def fetch_content(self) -> list[Data]:\n        urls = [self.ensure_url(url.strip()) for url in self.urls if url.strip()]\n        if self.format == \"Raw HTML\":\n            loader = AsyncHtmlLoader(web_path=urls, encoding=\"utf-8\")\n        else:\n            loader = WebBaseLoader(web_paths=urls, encoding=\"utf-8\")\n        docs = loader.load()\n        data = [Data(text=doc.page_content, **doc.metadata) for doc in docs]\n        self.status = data\n        return data\n\n    def fetch_content_text(self) -> Message:\n        data = self.fetch_content()\n\n        result_string = data_to_text(\"{text}\", data)\n        self.status = result_string\n        return Message(text=result_string)\n\n    def as_dataframe(self) -> DataFrame:\n        return DataFrame(self.fetch_content())\n"
               },
               "format": {
                 "_input_type": "DropdownInput",

src/backend/base/langflow/initial_setup/starter_projects/Document Q&A.json

@@ -721,7 +721,7 @@
             "outputs": [
               {
                 "cache": true,
-                "display_name": "Text",
+                "display_name": "Message",
                 "method": "parse_data",
                 "name": "text",
                 "selected": "Message",
@@ -761,7 +761,7 @@
                 "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 = \"Parse Data\"\n    description = \"Convert Data into plain text following a specified template.\"\n    icon = \"braces\"\n    name = \"ParseData\"\n\n    inputs = [\n        DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\", is_list=True),\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        ),\n        StrInput(name=\"sep\", display_name=\"Separator\", advanced=True, value=\"\\n\"),\n    ]\n\n    outputs = [\n        Output(\n            display_name=\"Text\",\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 = \"Parse Data\"\n    description = \"Convert Data into plain text following a specified template.\"\n    icon = \"braces\"\n    name = \"ParseData\"\n\n    inputs = [\n        DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\", is_list=True),\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        ),\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"
               },
               "data": {
                 "advanced": false,
@@ -905,7 +905,7 @@
             "outputs": [
               {
                 "cache": true,
-                "display_name": "Text",
+                "display_name": "Message",
                 "method": "text_response",
                 "name": "text_output",
                 "required_inputs": [],

src/backend/base/langflow/initial_setup/starter_projects/Graph Vector Store RAG.json

@@ -1678,7 +1678,7 @@
             "outputs": [
               {
                 "cache": true,
-                "display_name": "Text",
+                "display_name": "Message",
                 "method": "parse_data",
                 "name": "text",
                 "selected": "Message",
@@ -1718,7 +1718,7 @@
                 "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 = \"Parse Data\"\n    description = \"Convert Data into plain text following a specified template.\"\n    icon = \"braces\"\n    name = \"ParseData\"\n\n    inputs = [\n        DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\", is_list=True),\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        ),\n        StrInput(name=\"sep\", display_name=\"Separator\", advanced=True, value=\"\\n\"),\n    ]\n\n    outputs = [\n        Output(\n            display_name=\"Text\",\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 = \"Parse Data\"\n    description = \"Convert Data into plain text following a specified template.\"\n    icon = \"braces\"\n    name = \"ParseData\"\n\n    inputs = [\n        DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\", is_list=True),\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        ),\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"
               },
               "data": {
                 "_input_type": "DataInput",
@@ -2004,7 +2004,7 @@
             "outputs": [
               {
                 "cache": true,
-                "display_name": "Text",
+                "display_name": "Message",
                 "method": "text_response",
                 "name": "text_output",
                 "required_inputs": [],
@@ -2599,7 +2599,7 @@
               },
               {
                 "cache": true,
-                "display_name": "Text",
+                "display_name": "Message",
                 "method": "fetch_content_text",
                 "name": "text",
                 "selected": "Message",
@@ -2640,7 +2640,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import re\n\nfrom langchain_community.document_loaders import AsyncHtmlLoader, WebBaseLoader\n\nfrom langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\n\n\nclass URLComponent(Component):\n    display_name = \"URL\"\n    description = \"Load and retrive data from specified URLs.\"\n    icon = \"layout-template\"\n    name = \"URL\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"urls\",\n            display_name=\"URLs\",\n            is_list=True,\n            tool_mode=True,\n            placeholder=\"Enter a URL...\",\n            list_add_label=\"Add URL\",\n        ),\n        DropdownInput(\n            name=\"format\",\n            display_name=\"Output Format\",\n            info=\"Output Format. Use 'Text' to extract the text from the HTML or 'Raw HTML' for the raw HTML content.\",\n            options=[\"Text\", \"Raw HTML\"],\n            value=\"Text\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"data\", method=\"fetch_content\"),\n        Output(display_name=\"Text\", name=\"text\", method=\"fetch_content_text\"),\n        Output(display_name=\"DataFrame\", name=\"dataframe\", method=\"as_dataframe\"),\n    ]\n\n    def ensure_url(self, string: str) -> str:\n        \"\"\"Ensures the given string is a URL by adding 'http://' if it doesn't start with 'http://' or 'https://'.\n\n        Raises an error if the string is not a valid URL.\n\n        Parameters:\n            string (str): The string to be checked and possibly modified.\n\n        Returns:\n            str: The modified string that is ensured to be a URL.\n\n        Raises:\n            ValueError: If the string is not a valid URL.\n        \"\"\"\n        if not string.startswith((\"http://\", \"https://\")):\n            string = \"http://\" + string\n\n        # Basic URL validation regex\n        url_regex = re.compile(\n            r\"^(https?:\\/\\/)?\"  # optional protocol\n            r\"(www\\.)?\"  # optional www\n            r\"([a-zA-Z0-9.-]+)\"  # domain\n            r\"(\\.[a-zA-Z]{2,})?\"  # top-level domain\n            r\"(:\\d+)?\"  # optional port\n            r\"(\\/[^\\s]*)?$\",  # optional path\n            re.IGNORECASE,\n        )\n\n        if not url_regex.match(string):\n            msg = f\"Invalid URL: {string}\"\n            raise ValueError(msg)\n\n        return string\n\n    def fetch_content(self) -> list[Data]:\n        urls = [self.ensure_url(url.strip()) for url in self.urls if url.strip()]\n        if self.format == \"Raw HTML\":\n            loader = AsyncHtmlLoader(web_path=urls, encoding=\"utf-8\")\n        else:\n            loader = WebBaseLoader(web_paths=urls, encoding=\"utf-8\")\n        docs = loader.load()\n        data = [Data(text=doc.page_content, **doc.metadata) for doc in docs]\n        self.status = data\n        return data\n\n    def fetch_content_text(self) -> Message:\n        data = self.fetch_content()\n\n        result_string = data_to_text(\"{text}\", data)\n        self.status = result_string\n        return Message(text=result_string)\n\n    def as_dataframe(self) -> DataFrame:\n        return DataFrame(self.fetch_content())\n"
+                "value": "import re\n\nfrom langchain_community.document_loaders import AsyncHtmlLoader, WebBaseLoader\n\nfrom langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\n\n\nclass URLComponent(Component):\n    display_name = \"URL\"\n    description = \"Load and retrive data from specified URLs.\"\n    icon = \"layout-template\"\n    name = \"URL\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"urls\",\n            display_name=\"URLs\",\n            is_list=True,\n            tool_mode=True,\n            placeholder=\"Enter a URL...\",\n            list_add_label=\"Add URL\",\n        ),\n        DropdownInput(\n            name=\"format\",\n            display_name=\"Output Format\",\n            info=\"Output Format. Use 'Text' to extract the text from the HTML or 'Raw HTML' for the raw HTML content.\",\n            options=[\"Text\", \"Raw HTML\"],\n            value=\"Text\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"data\", method=\"fetch_content\"),\n        Output(display_name=\"Message\", name=\"text\", method=\"fetch_content_text\"),\n        Output(display_name=\"DataFrame\", name=\"dataframe\", method=\"as_dataframe\"),\n    ]\n\n    def ensure_url(self, string: str) -> str:\n        \"\"\"Ensures the given string is a URL by adding 'http://' if it doesn't start with 'http://' or 'https://'.\n\n        Raises an error if the string is not a valid URL.\n\n        Parameters:\n            string (str): The string to be checked and possibly modified.\n\n        Returns:\n            str: The modified string that is ensured to be a URL.\n\n        Raises:\n            ValueError: If the string is not a valid URL.\n        \"\"\"\n        if not string.startswith((\"http://\", \"https://\")):\n            string = \"http://\" + string\n\n        # Basic URL validation regex\n        url_regex = re.compile(\n            r\"^(https?:\\/\\/)?\"  # optional protocol\n            r\"(www\\.)?\"  # optional www\n            r\"([a-zA-Z0-9.-]+)\"  # domain\n            r\"(\\.[a-zA-Z]{2,})?\"  # top-level domain\n            r\"(:\\d+)?\"  # optional port\n            r\"(\\/[^\\s]*)?$\",  # optional path\n            re.IGNORECASE,\n        )\n\n        if not url_regex.match(string):\n            msg = f\"Invalid URL: {string}\"\n            raise ValueError(msg)\n\n        return string\n\n    def fetch_content(self) -> list[Data]:\n        urls = [self.ensure_url(url.strip()) for url in self.urls if url.strip()]\n        if self.format == \"Raw HTML\":\n            loader = AsyncHtmlLoader(web_path=urls, encoding=\"utf-8\")\n        else:\n            loader = WebBaseLoader(web_paths=urls, encoding=\"utf-8\")\n        docs = loader.load()\n        data = [Data(text=doc.page_content, **doc.metadata) for doc in docs]\n        self.status = data\n        return data\n\n    def fetch_content_text(self) -> Message:\n        data = self.fetch_content()\n\n        result_string = data_to_text(\"{text}\", data)\n        self.status = result_string\n        return Message(text=result_string)\n\n    def as_dataframe(self) -> DataFrame:\n        return DataFrame(self.fetch_content())\n"
               },
               "format": {
                 "_input_type": "DropdownInput",

src/backend/base/langflow/initial_setup/starter_projects/Image Sentiment Analysis.json

@@ -1025,7 +1025,7 @@
             "outputs": [
               {
                 "cache": true,
-                "display_name": "Text",
+                "display_name": "Message",
                 "method": "parse_data",
                 "name": "text",
                 "selected": "Message",
@@ -1065,7 +1065,7 @@
                 "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 = \"Parse Data\"\n    description = \"Convert Data into plain text following a specified template.\"\n    icon = \"braces\"\n    name = \"ParseData\"\n\n    inputs = [\n        DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\", is_list=True),\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        ),\n        StrInput(name=\"sep\", display_name=\"Separator\", advanced=True, value=\"\\n\"),\n    ]\n\n    outputs = [\n        Output(\n            display_name=\"Text\",\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 = \"Parse Data\"\n    description = \"Convert Data into plain text following a specified template.\"\n    icon = \"braces\"\n    name = \"ParseData\"\n\n    inputs = [\n        DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\", is_list=True),\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        ),\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"
               },
               "data": {
                 "_input_type": "DataInput",
@@ -1189,7 +1189,7 @@
             "outputs": [
               {
                 "cache": true,
-                "display_name": "Text",
+                "display_name": "Message",
                 "method": "text_response",
                 "name": "text_output",
                 "required_inputs": [],

src/backend/base/langflow/initial_setup/starter_projects/Instagram Copywriter.json

@@ -767,7 +767,7 @@
             "outputs": [
               {
                 "cache": true,
-                "display_name": "Text",
+                "display_name": "Message",
                 "method": "text_response",
                 "name": "text",
                 "selected": "Message",
@@ -796,7 +796,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.text import TextComponent\nfrom langflow.io import MultilineInput, Output\nfrom langflow.schema.message import Message\n\n\nclass TextInputComponent(TextComponent):\n    display_name = \"Text Input\"\n    description = \"Get text inputs from the Playground.\"\n    icon = \"type\"\n    name = \"TextInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Text to be passed as input.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"text_response\"),\n    ]\n\n    def text_response(self) -> Message:\n        return Message(\n            text=self.input_value,\n        )\n"
+                "value": "from langflow.base.io.text import TextComponent\nfrom langflow.io import MultilineInput, Output\nfrom langflow.schema.message import Message\n\n\nclass TextInputComponent(TextComponent):\n    display_name = \"Text Input\"\n    description = \"Get text inputs from the Playground.\"\n    icon = \"type\"\n    name = \"TextInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Text to be passed as input.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"text\", method=\"text_response\"),\n    ]\n\n    def text_response(self) -> Message:\n        return Message(\n            text=self.input_value,\n        )\n"
               },
               "input_value": {
                 "_input_type": "MultilineInput",
@@ -884,7 +884,7 @@
             "outputs": [
               {
                 "cache": true,
-                "display_name": "Text",
+                "display_name": "Message",
                 "method": "text_response",
                 "name": "text_output",
                 "required_inputs": [],
@@ -2209,7 +2209,7 @@
             "outputs": [
               {
                 "cache": true,
-                "display_name": "Text",
+                "display_name": "Message",
                 "method": "text_response",
                 "name": "text_output",
                 "required_inputs": [],

src/backend/base/langflow/initial_setup/starter_projects/Market Research.json

@@ -1177,7 +1177,7 @@
             "outputs": [
               {
                 "cache": true,
-                "display_name": "Text",
+                "display_name": "Message",
                 "method": "text_response",
                 "name": "text_output",
                 "required_inputs": [],
@@ -1482,7 +1482,7 @@
             "outputs": [
               {
                 "cache": true,
-                "display_name": "Text",
+                "display_name": "Message",
                 "method": "parse_data",
                 "name": "text",
                 "selected": "Message",
@@ -1522,7 +1522,7 @@
                 "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 = \"Parse Data\"\n    description = \"Convert Data into plain text following a specified template.\"\n    icon = \"braces\"\n    name = \"ParseData\"\n\n    inputs = [\n        DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\", is_list=True),\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        ),\n        StrInput(name=\"sep\", display_name=\"Separator\", advanced=True, value=\"\\n\"),\n    ]\n\n    outputs = [\n        Output(\n            display_name=\"Text\",\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 = \"Parse Data\"\n    description = \"Convert Data into plain text following a specified template.\"\n    icon = \"braces\"\n    name = \"ParseData\"\n\n    inputs = [\n        DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\", is_list=True),\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        ),\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"
               },
               "data": {
                 "_input_type": "DataInput",

src/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.json

@@ -782,7 +782,7 @@
             "outputs": [
               {
                 "cache": true,
-                "display_name": "Text",
+                "display_name": "Message",
                 "method": "text_response",
                 "name": "text_output",
                 "required_inputs": [],
@@ -1097,7 +1097,7 @@
               },
               {
                 "cache": true,
-                "display_name": "Text",
+                "display_name": "Message",
                 "method": "retrieve_messages_as_text",
                 "name": "messages_text",
                 "selected": "Message",
@@ -1126,7 +1126,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.inputs import HandleInput\nfrom langflow.io import DropdownInput, IntInput, MessageTextInput, MultilineInput, Output\nfrom langflow.memory import aget_messages\nfrom langflow.schema import Data\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_USER\n\n\nclass MemoryComponent(Component):\n    display_name = \"Message History\"\n    description = \"Retrieves stored chat messages from Langflow tables or an external memory.\"\n    icon = \"message-square-more\"\n    name = \"Memory\"\n\n    inputs = [\n        HandleInput(\n            name=\"memory\",\n            display_name=\"External Memory\",\n            input_types=[\"Memory\"],\n            info=\"Retrieve messages from an external memory. If empty, it will use the Langflow tables.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER, \"Machine and User\"],\n            value=\"Machine and User\",\n            info=\"Filter by sender type.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Filter by sender name.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Messages\",\n            value=100,\n            info=\"Number of messages to retrieve.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"order\",\n            display_name=\"Order\",\n            options=[\"Ascending\", \"Descending\"],\n            value=\"Ascending\",\n            info=\"Order of the messages.\",\n            advanced=True,\n            tool_mode=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}, {sender} or any other key in the message data.\",\n            value=\"{sender_name}: {text}\",\n            advanced=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"messages\", method=\"retrieve_messages\"),\n        Output(display_name=\"Text\", name=\"messages_text\", method=\"retrieve_messages_as_text\"),\n    ]\n\n    async def retrieve_messages(self) -> Data:\n        sender = self.sender\n        sender_name = self.sender_name\n        session_id = self.session_id\n        n_messages = self.n_messages\n        order = \"DESC\" if self.order == \"Descending\" else \"ASC\"\n\n        if sender == \"Machine and User\":\n            sender = None\n\n        if self.memory:\n            # override session_id\n            self.memory.session_id = session_id\n\n            stored = await self.memory.aget_messages()\n            # langchain memories are supposed to return messages in ascending order\n            if order == \"DESC\":\n                stored = stored[::-1]\n            if n_messages:\n                stored = stored[:n_messages]\n            stored = [Message.from_lc_message(m) for m in stored]\n            if sender:\n                expected_type = MESSAGE_SENDER_AI if sender == MESSAGE_SENDER_AI else MESSAGE_SENDER_USER\n                stored = [m for m in stored if m.type == expected_type]\n        else:\n            stored = await aget_messages(\n                sender=sender,\n                sender_name=sender_name,\n                session_id=session_id,\n                limit=n_messages,\n                order=order,\n            )\n        self.status = stored\n        return stored\n\n    async def retrieve_messages_as_text(self) -> Message:\n        stored_text = data_to_text(self.template, await self.retrieve_messages())\n        self.status = stored_text\n        return Message(text=stored_text)\n"
+                "value": "from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.inputs import HandleInput\nfrom langflow.io import DropdownInput, IntInput, MessageTextInput, MultilineInput, Output\nfrom langflow.memory import aget_messages\nfrom langflow.schema import Data\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_USER\n\n\nclass MemoryComponent(Component):\n    display_name = \"Message History\"\n    description = \"Retrieves stored chat messages from Langflow tables or an external memory.\"\n    icon = \"message-square-more\"\n    name = \"Memory\"\n\n    inputs = [\n        HandleInput(\n            name=\"memory\",\n            display_name=\"External Memory\",\n            input_types=[\"Memory\"],\n            info=\"Retrieve messages from an external memory. If empty, it will use the Langflow tables.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER, \"Machine and User\"],\n            value=\"Machine and User\",\n            info=\"Filter by sender type.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Filter by sender name.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Messages\",\n            value=100,\n            info=\"Number of messages to retrieve.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"order\",\n            display_name=\"Order\",\n            options=[\"Ascending\", \"Descending\"],\n            value=\"Ascending\",\n            info=\"Order of the messages.\",\n            advanced=True,\n            tool_mode=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}, {sender} or any other key in the message data.\",\n            value=\"{sender_name}: {text}\",\n            advanced=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"messages\", method=\"retrieve_messages\"),\n        Output(display_name=\"Message\", name=\"messages_text\", method=\"retrieve_messages_as_text\"),\n    ]\n\n    async def retrieve_messages(self) -> Data:\n        sender = self.sender\n        sender_name = self.sender_name\n        session_id = self.session_id\n        n_messages = self.n_messages\n        order = \"DESC\" if self.order == \"Descending\" else \"ASC\"\n\n        if sender == \"Machine and User\":\n            sender = None\n\n        if self.memory:\n            # override session_id\n            self.memory.session_id = session_id\n\n            stored = await self.memory.aget_messages()\n            # langchain memories are supposed to return messages in ascending order\n            if order == \"DESC\":\n                stored = stored[::-1]\n            if n_messages:\n                stored = stored[:n_messages]\n            stored = [Message.from_lc_message(m) for m in stored]\n            if sender:\n                expected_type = MESSAGE_SENDER_AI if sender == MESSAGE_SENDER_AI else MESSAGE_SENDER_USER\n                stored = [m for m in stored if m.type == expected_type]\n        else:\n            stored = await aget_messages(\n                sender=sender,\n                sender_name=sender_name,\n                session_id=session_id,\n                limit=n_messages,\n                order=order,\n            )\n        self.status = stored\n        return stored\n\n    async def retrieve_messages_as_text(self) -> Message:\n        stored_text = data_to_text(self.template, await self.retrieve_messages())\n        self.status = stored_text\n        return Message(text=stored_text)\n"
               },
               "memory": {
                 "_input_type": "HandleInput",

src/backend/base/langflow/initial_setup/starter_projects/Research Agent.json

@@ -1442,7 +1442,7 @@
             "outputs": [
               {
                 "cache": true,
-                "display_name": "Text",
+                "display_name": "Message",
                 "method": "text_response",
                 "name": "text_output",
                 "required_inputs": [],
@@ -1754,7 +1754,7 @@
             "outputs": [
               {
                 "cache": true,
-                "display_name": "Text",
+                "display_name": "Message",
                 "method": "text_response",
                 "name": "text_output",
                 "required_inputs": [],

src/backend/base/langflow/initial_setup/starter_projects/SEO Keyword Generator.json

@@ -823,7 +823,7 @@
             "outputs": [
               {
                 "cache": true,
-                "display_name": "Text",
+                "display_name": "Message",
                 "method": "text_response",
                 "name": "text_output",
                 "required_inputs": [],

src/backend/base/langflow/initial_setup/starter_projects/Twitter Thread Generator.json

@@ -566,7 +566,7 @@
             "outputs": [
               {
                 "cache": true,
-                "display_name": "Text",
+                "display_name": "Message",
                 "method": "text_response",
                 "name": "text",
                 "selected": "Message",
@@ -595,7 +595,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.text import TextComponent\nfrom langflow.io import MultilineInput, Output\nfrom langflow.schema.message import Message\n\n\nclass TextInputComponent(TextComponent):\n    display_name = \"Text Input\"\n    description = \"Get text inputs from the Playground.\"\n    icon = \"type\"\n    name = \"TextInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Text to be passed as input.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"text_response\"),\n    ]\n\n    def text_response(self) -> Message:\n        return Message(\n            text=self.input_value,\n        )\n"
+                "value": "from langflow.base.io.text import TextComponent\nfrom langflow.io import MultilineInput, Output\nfrom langflow.schema.message import Message\n\n\nclass TextInputComponent(TextComponent):\n    display_name = \"Text Input\"\n    description = \"Get text inputs from the Playground.\"\n    icon = \"type\"\n    name = \"TextInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Text to be passed as input.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"text\", method=\"text_response\"),\n    ]\n\n    def text_response(self) -> Message:\n        return Message(\n            text=self.input_value,\n        )\n"
               },
               "input_value": {
                 "_input_type": "MultilineInput",
@@ -679,7 +679,7 @@
             "outputs": [
               {
                 "cache": true,
-                "display_name": "Text",
+                "display_name": "Message",
                 "method": "text_response",
                 "name": "text_output",
                 "required_inputs": [],
@@ -1253,7 +1253,7 @@
             "outputs": [
               {
                 "cache": true,
-                "display_name": "Text",
+                "display_name": "Message",
                 "method": "text_response",
                 "name": "text",
                 "selected": "Message",
@@ -1282,7 +1282,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.text import TextComponent\nfrom langflow.io import MultilineInput, Output\nfrom langflow.schema.message import Message\n\n\nclass TextInputComponent(TextComponent):\n    display_name = \"Text Input\"\n    description = \"Get text inputs from the Playground.\"\n    icon = \"type\"\n    name = \"TextInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Text to be passed as input.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"text_response\"),\n    ]\n\n    def text_response(self) -> Message:\n        return Message(\n            text=self.input_value,\n        )\n"
+                "value": "from langflow.base.io.text import TextComponent\nfrom langflow.io import MultilineInput, Output\nfrom langflow.schema.message import Message\n\n\nclass TextInputComponent(TextComponent):\n    display_name = \"Text Input\"\n    description = \"Get text inputs from the Playground.\"\n    icon = \"type\"\n    name = \"TextInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Text to be passed as input.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"text\", method=\"text_response\"),\n    ]\n\n    def text_response(self) -> Message:\n        return Message(\n            text=self.input_value,\n        )\n"
               },
               "input_value": {
                 "_input_type": "MultilineInput",
@@ -1351,7 +1351,7 @@
             "outputs": [
               {
                 "cache": true,
-                "display_name": "Text",
+                "display_name": "Message",
                 "method": "text_response",
                 "name": "text",
                 "selected": "Message",
@@ -1380,7 +1380,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.text import TextComponent\nfrom langflow.io import MultilineInput, Output\nfrom langflow.schema.message import Message\n\n\nclass TextInputComponent(TextComponent):\n    display_name = \"Text Input\"\n    description = \"Get text inputs from the Playground.\"\n    icon = \"type\"\n    name = \"TextInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Text to be passed as input.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"text_response\"),\n    ]\n\n    def text_response(self) -> Message:\n        return Message(\n            text=self.input_value,\n        )\n"
+                "value": "from langflow.base.io.text import TextComponent\nfrom langflow.io import MultilineInput, Output\nfrom langflow.schema.message import Message\n\n\nclass TextInputComponent(TextComponent):\n    display_name = \"Text Input\"\n    description = \"Get text inputs from the Playground.\"\n    icon = \"type\"\n    name = \"TextInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Text to be passed as input.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"text\", method=\"text_response\"),\n    ]\n\n    def text_response(self) -> Message:\n        return Message(\n            text=self.input_value,\n        )\n"
               },
               "input_value": {
                 "_input_type": "MultilineInput",
@@ -1449,7 +1449,7 @@
             "outputs": [
               {
                 "cache": true,
-                "display_name": "Text",
+                "display_name": "Message",
                 "method": "text_response",
                 "name": "text",
                 "selected": "Message",
@@ -1478,7 +1478,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.text import TextComponent\nfrom langflow.io import MultilineInput, Output\nfrom langflow.schema.message import Message\n\n\nclass TextInputComponent(TextComponent):\n    display_name = \"Text Input\"\n    description = \"Get text inputs from the Playground.\"\n    icon = \"type\"\n    name = \"TextInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Text to be passed as input.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"text_response\"),\n    ]\n\n    def text_response(self) -> Message:\n        return Message(\n            text=self.input_value,\n        )\n"
+                "value": "from langflow.base.io.text import TextComponent\nfrom langflow.io import MultilineInput, Output\nfrom langflow.schema.message import Message\n\n\nclass TextInputComponent(TextComponent):\n    display_name = \"Text Input\"\n    description = \"Get text inputs from the Playground.\"\n    icon = \"type\"\n    name = \"TextInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Text to be passed as input.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"text\", method=\"text_response\"),\n    ]\n\n    def text_response(self) -> Message:\n        return Message(\n            text=self.input_value,\n        )\n"
               },
               "input_value": {
                 "_input_type": "MultilineInput",
@@ -1547,7 +1547,7 @@
             "outputs": [
               {
                 "cache": true,
-                "display_name": "Text",
+                "display_name": "Message",
                 "method": "text_response",
                 "name": "text",
                 "selected": "Message",
@@ -1576,7 +1576,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.text import TextComponent\nfrom langflow.io import MultilineInput, Output\nfrom langflow.schema.message import Message\n\n\nclass TextInputComponent(TextComponent):\n    display_name = \"Text Input\"\n    description = \"Get text inputs from the Playground.\"\n    icon = \"type\"\n    name = \"TextInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Text to be passed as input.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"text_response\"),\n    ]\n\n    def text_response(self) -> Message:\n        return Message(\n            text=self.input_value,\n        )\n"
+                "value": "from langflow.base.io.text import TextComponent\nfrom langflow.io import MultilineInput, Output\nfrom langflow.schema.message import Message\n\n\nclass TextInputComponent(TextComponent):\n    display_name = \"Text Input\"\n    description = \"Get text inputs from the Playground.\"\n    icon = \"type\"\n    name = \"TextInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Text to be passed as input.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"text\", method=\"text_response\"),\n    ]\n\n    def text_response(self) -> Message:\n        return Message(\n            text=self.input_value,\n        )\n"
               },
               "input_value": {
                 "_input_type": "MultilineInput",
@@ -1645,7 +1645,7 @@
             "outputs": [
               {
                 "cache": true,
-                "display_name": "Text",
+                "display_name": "Message",
                 "method": "text_response",
                 "name": "text",
                 "selected": "Message",
@@ -1674,7 +1674,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.text import TextComponent\nfrom langflow.io import MultilineInput, Output\nfrom langflow.schema.message import Message\n\n\nclass TextInputComponent(TextComponent):\n    display_name = \"Text Input\"\n    description = \"Get text inputs from the Playground.\"\n    icon = \"type\"\n    name = \"TextInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Text to be passed as input.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"text_response\"),\n    ]\n\n    def text_response(self) -> Message:\n        return Message(\n            text=self.input_value,\n        )\n"
+                "value": "from langflow.base.io.text import TextComponent\nfrom langflow.io import MultilineInput, Output\nfrom langflow.schema.message import Message\n\n\nclass TextInputComponent(TextComponent):\n    display_name = \"Text Input\"\n    description = \"Get text inputs from the Playground.\"\n    icon = \"type\"\n    name = \"TextInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Text to be passed as input.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"text\", method=\"text_response\"),\n    ]\n\n    def text_response(self) -> Message:\n        return Message(\n            text=self.input_value,\n        )\n"
               },
               "input_value": {
                 "_input_type": "MultilineInput",

src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json

@@ -574,7 +574,7 @@
             "outputs": [
               {
                 "cache": true,
-                "display_name": "Text",
+                "display_name": "Message",
                 "method": "parse_data",
                 "name": "text",
                 "selected": "Message",
@@ -614,7 +614,7 @@
                 "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 = \"Parse Data\"\n    description = \"Convert Data into plain text following a specified template.\"\n    icon = \"braces\"\n    name = \"ParseData\"\n\n    inputs = [\n        DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\", is_list=True),\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        ),\n        StrInput(name=\"sep\", display_name=\"Separator\", advanced=True, value=\"\\n\"),\n    ]\n\n    outputs = [\n        Output(\n            display_name=\"Text\",\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 = \"Parse Data\"\n    description = \"Convert Data into plain text following a specified template.\"\n    icon = \"braces\"\n    name = \"ParseData\"\n\n    inputs = [\n        DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\", is_list=True),\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        ),\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"
               },
               "data": {
                 "advanced": false,
@@ -1130,7 +1130,7 @@
             "outputs": [
               {
                 "cache": true,
-                "display_name": "Text",
+                "display_name": "Message",
                 "method": "text_response",
                 "name": "text_output",
                 "required_inputs": [],

src/backend/base/langflow/inputs/inputs.py

@@ -483,7 +483,7 @@ class SliderInput(BaseInputMixin, RangeMixin, SliderMixin, ToolModeMixin):
     field_type: SerializableFieldTypes = FieldTypes.SLIDER
 
 
-DEFAULT_PROMPT_INTUT_TYPES = ["Message", "Text"]
+DEFAULT_PROMPT_INTUT_TYPES = ["Message"]
 
 
 class DefaultPromptField(Input):

src/backend/tests/unit/graph/graph/test_cycles.py

@@ -22,7 +22,7 @@ class Concatenate(Component):
         MessageTextInput(name="text", display_name="Text", required=True),
     ]
     outputs = [
-        Output(display_name="Text", name="some_text", method="concatenate"),
+        Output(display_name="Message", name="some_text", method="concatenate"),
     ]
 
     def concatenate(self) -> Message:

src/frontend/src/CustomNodes/GenericNode/components/NodeOutputfield/index.tsx

@@ -101,6 +101,7 @@ const InspectButton = memo(
     isToolMode,
     title,
     onClick,
+    id,
   }: {
     disabled: boolean | undefined;
     displayOutputPreview: boolean;
@@ -109,10 +110,11 @@ const InspectButton = memo(
     isToolMode: boolean;
     title: string;
     onClick: () => void;
+    id: string;
   }) => (
     <Button
       disabled={disabled}
-      data-testid={`output-inspection-${title.toLowerCase()}`}
+      data-testid={`output-inspection-${title.toLowerCase()}-${id.toLowerCase()}`}
       unstyled
       onClick={onClick}
     >
@@ -338,6 +340,7 @@ function NodeOutputField({
                   onClick={() => {
                     //just to trigger the memoization
                   }}
+                  id={data?.type}
                 />
               </OutputModal>
             </div>

src/frontend/tests/core/features/freeze-path.spec.ts

@@ -65,14 +65,20 @@ test(
       timeout: 15000,
     });
 
-    await page.getByTestId("output-inspection-text").first().click();
+    await page
+      .getByTestId("output-inspection-message-chatoutput")
+      .first()
+      .click();
+
+    await page.getByRole("gridcell").nth(4).click();
 
     const randomTextGeneratedByAI = await page
       .getByPlaceholder("Empty")
       .first()
       .inputValue();
 
-    await page.getByText("Close").first().click();
+    await page.getByText("Close").last().click();
+    await page.getByText("Close").last().click();
 
     await page.waitForSelector('[data-testid="default_slider_display_value"]', {
       timeout: 1000,
@@ -95,14 +101,20 @@ test(
       timeout: 15000,
     });
 
-    await page.getByTestId("output-inspection-text").first().click();
+    await page
+      .getByTestId("output-inspection-message-chatoutput")
+      .first()
+      .click();
+
+    await page.getByRole("gridcell").nth(4).click();
 
     const secondRandomTextGeneratedByAI = await page
       .getByPlaceholder("Empty")
       .first()
       .inputValue();
 
-    await page.getByText("Close").first().click();
+    await page.getByText("Close").last().click();
+    await page.getByText("Close").last().click();
 
     await page.waitForSelector("text=OpenAI", {
       timeout: 1000,
@@ -140,14 +152,20 @@ test(
       timeout: 15000,
     });
 
-    await page.getByTestId("output-inspection-text").first().click();
+    await page
+      .getByTestId("output-inspection-message-chatoutput")
+      .first()
+      .click();
+
+    await page.getByRole("gridcell").nth(4).click();
 
     const thirdRandomTextGeneratedByAI = await page
       .getByPlaceholder("Empty")
       .first()
       .inputValue();
 
-    await page.getByText("Close").first().click();
+    await page.getByText("Close").last().click();
+    await page.getByText("Close").last().click();
 
     expect(randomTextGeneratedByAI).not.toEqual(secondRandomTextGeneratedByAI);
     expect(randomTextGeneratedByAI).not.toEqual(thirdRandomTextGeneratedByAI);

src/frontend/tests/core/features/freeze.spec.ts

@@ -126,7 +126,7 @@ test(
 
     //connection 2
     const textOutput = await page
-      .getByTestId("handle-textinput-shownode-text-right")
+      .getByTestId("handle-textinput-shownode-message-right")
       .nth(0);
     await textOutput.hover();
     await page.mouse.down();
@@ -150,7 +150,7 @@ test(
 
     //connection 4
     const parseDataOutput = await page
-      .getByTestId("handle-parsedata-shownode-text-right")
+      .getByTestId("handle-parsedata-shownode-message-right")
       .nth(0);
     await parseDataOutput.hover();
     await page.mouse.down();
@@ -177,11 +177,17 @@ test(
       timeout: 15000,
     });
 
-    await page.waitForSelector('[data-testid="output-inspection-message"]', {
-      timeout: 1000,
-    });
+    await page.waitForSelector(
+      '[data-testid="output-inspection-message-chatoutput"]',
+      {
+        timeout: 1000,
+      },
+    );
 
-    await page.getByTestId("output-inspection-message").first().click();
+    await page
+      .getByTestId("output-inspection-message-chatoutput")
+      .first()
+      .click();
 
     await page.getByRole("gridcell").nth(4).click();
 
@@ -203,11 +209,17 @@ test(
       timeout: 15000,
     });
 
-    await page.waitForSelector('[data-testid="output-inspection-message"]', {
-      timeout: 1000,
-    });
+    await page.waitForSelector(
+      '[data-testid="output-inspection-message-chatoutput"]',
+      {
+        timeout: 1000,
+      },
+    );
 
-    await page.getByTestId("output-inspection-message").first().click();
+    await page
+      .getByTestId("output-inspection-message-chatoutput")
+      .first()
+      .click();
 
     await page.getByRole("gridcell").nth(4).click();
 
@@ -253,11 +265,17 @@ test(
       timeout: 15000,
     });
 
-    await page.waitForSelector('[data-testid="output-inspection-message"]', {
-      timeout: 1000,
-    });
+    await page.waitForSelector(
+      '[data-testid="output-inspection-message-chatoutput"]',
+      {
+        timeout: 1000,
+      },
+    );
 
-    await page.getByTestId("output-inspection-message").first().click();
+    await page
+      .getByTestId("output-inspection-message-chatoutput")
+      .first()
+      .click();
 
     await page.getByRole("gridcell").nth(4).click();
 
@@ -292,11 +310,17 @@ test(
       timeout: 15000,
     });
 
-    await page.waitForSelector('[data-testid="output-inspection-message"]', {
-      timeout: 1000,
-    });
+    await page.waitForSelector(
+      '[data-testid="output-inspection-message-chatoutput"]',
+      {
+        timeout: 1000,
+      },
+    );
 
-    await page.getByTestId("output-inspection-message").first().click();
+    await page
+      .getByTestId("output-inspection-message-chatoutput")
+      .first()
+      .click();
 
     await page.getByRole("gridcell").nth(4).click();
 

src/frontend/tests/core/features/playground.spec.ts

@@ -117,7 +117,7 @@ test(
     //
 
     const elementsTextOutputRight = await page
-      .locator('[data-testid="handle-textoutput-shownode-text-right"]')
+      .locator('[data-testid="handle-textoutput-shownode-message-right"]')
       .all();
 
     for (const element of elementsTextOutputRight) {

src/frontend/tests/core/features/stop-building.spec.ts

@@ -89,7 +89,7 @@ test(
 
     //connection 2
     const textOutput = await page
-      .getByTestId("handle-textinput-shownode-text-right")
+      .getByTestId("handle-textinput-shownode-message-right")
       .nth(0);
     await textOutput.hover();
     await page.mouse.down();
@@ -113,7 +113,7 @@ test(
 
     //connection 4
     const parseDataOutput = await page
-      .getByTestId("handle-parsedata-shownode-text-right")
+      .getByTestId("handle-parsedata-shownode-message-right")
       .nth(0);
     await parseDataOutput.hover();
     await page.mouse.down();

src/frontend/tests/core/integrations/decisionFlow.spec.ts

@@ -255,7 +255,7 @@ test(
       .nth(2)
       .click();
     await page
-      .getByTestId("handle-parsedata-shownode-text-right")
+      .getByTestId("handle-parsedata-shownode-message-right")
       .nth(0)
       .click();
     //quebrando aqui
@@ -264,7 +264,7 @@ test(
       .nth(0)
       .click();
     await page
-      .getByTestId("handle-parsedata-shownode-text-right")
+      .getByTestId("handle-parsedata-shownode-message-right")
       .nth(2)
       .click();
     await page
@@ -288,7 +288,7 @@ test(
       .nth(0)
       .click();
     await page
-      .getByTestId("handle-openaimodel-shownode-text-right")
+      .getByTestId("handle-openaimodel-shownode-message-right")
       .nth(0)
       .click();
     await page

src/frontend/tests/core/integrations/similarity.spec.ts

@@ -229,7 +229,7 @@ test(
 
     //connection 7
     const parseDataOutput = await page
-      .getByTestId("handle-parsedata-shownode-text-right")
+      .getByTestId("handle-parsedata-shownode-message-right")
       .nth(0);
     await parseDataOutput.hover();
     await page.mouse.down();
@@ -245,7 +245,7 @@ test(
 
     await page
       .getByTestId(/rf__node-TextOutput-[a-zA-Z0-9]{5}/)
-      .getByTestId("output-inspection-text")
+      .getByTestId("output-inspection-message-textoutput")
       .first()
       .click();
     const valueSimilarity = await page.getByTestId("textarea").textContent();

src/frontend/tests/core/integrations/textInputOutput.spec.ts

@@ -148,7 +148,7 @@ test.skip(
     await page.getByTestId("button_run_text_output").click();
     await page
       .getByTestId(/^rf__node-TextOutput-[a-zA-Z0-9]+$/)
-      .getByTestId("output-inspection-text")
+      .getByTestId("output-inspection-message-chatoutput")
       .click();
     await page.getByText("Run Flow", { exact: true }).click();
     await page.waitForTimeout(5000);

src/frontend/tests/core/regression/generalBugs-shard-5.spec.ts

@@ -88,13 +88,13 @@ test(
     await elementCombineTextOutput0.click();
 
     const blockedHandle = page
-      .getByTestId("div-handle-textinput-shownode-text-right")
+      .getByTestId("div-handle-textinput-shownode-message-right")
       .first();
     const secondBlockedHandle = page
       .getByTestId("div-handle-combinetext-shownode-combined text-right")
       .nth(3);
     const thirdBlockedHandle = page
-      .getByTestId("div-handle-textoutput-shownode-text-right")
+      .getByTestId("div-handle-textoutput-shownode-message-right")
       .first();
 
     const hasGradient = await blockedHandle?.evaluate((el) => {
@@ -170,7 +170,7 @@ test(
     await page
       .getByTestId("title-Combine Text")
       .first()
-      .click({ modifiers: ["Control"] });
+      .click({ modifiers: ["ControlOrMeta"] });
 
     await page.waitForSelector('[data-testid="group-node"]', {
       timeout: 3000,
@@ -181,7 +181,7 @@ test(
 
     //connection 1
     const elementTextOutput0 = page
-      .getByTestId("handle-textinput-shownode-text-right")
+      .getByTestId("handle-textinput-shownode-message-right")
       .nth(0);
     await elementTextOutput0.click();
     const elementGroupInput0 = page.getByTestId(
@@ -191,7 +191,7 @@ test(
 
     //connection 2
     const elementTextOutput1 = page
-      .getByTestId("handle-textinput-shownode-text-right")
+      .getByTestId("handle-textinput-shownode-message-right")
       .nth(4);
     await elementTextOutput1.click();
     const elementGroupInput1 = page
@@ -201,7 +201,7 @@ test(
 
     //connection 3
     const elementTextOutput2 = page
-      .getByTestId("handle-textinput-shownode-text-right")
+      .getByTestId("handle-textinput-shownode-message-right")
       .nth(2);
     await elementTextOutput2.click();
 
@@ -243,9 +243,14 @@ test(
     });
 
     expect(
-      await page.getByTestId("output-inspection-combined text").first(),
+      await page
+        .getByTestId("output-inspection-combined text-groupnode")
+        .first(),
     ).not.toBeDisabled();
-    await page.getByTestId("output-inspection-combined text").first().click();
+    await page
+      .getByTestId("output-inspection-combined text-groupnode")
+      .first()
+      .click();
 
     await page.getByText("Component Output").isVisible();
 

src/frontend/tests/core/unit/fileUploadComponent.spec.ts

@@ -94,40 +94,14 @@ test(
     // Release the mouse
     await page.mouse.up();
 
-    // Click and hold on the first element
-
-    const parseDataOutputElement = await page
-      .getByTestId("handle-parsedata-shownode-text-right")
-      .all();
-
-    for (const element of parseDataOutputElement) {
-      if (await element.isVisible()) {
-        visibleElementHandle = element;
-        break;
-      }
-    }
-
-    await page.getByTitle("fit view").click();
-
-    await visibleElementHandle.hover();
-    await page.mouse.down();
-
-    // Move to the second element
-    const chatOutputElement = await page
+    await page
+      .getByTestId("handle-parsedata-shownode-message-right")
+      .first()
+      .click();
+    await page
       .getByTestId("handle-chatoutput-noshownode-text-target")
-      .all();
-
-    for (const element of chatOutputElement) {
-      if (await element.isVisible()) {
-        visibleElementHandle = element;
-        break;
-      }
-    }
-
-    await visibleElementHandle.hover();
-
-    // Release the mouse
-    await page.mouse.up();
+      .first()
+      .click();
 
     await page.getByText("Playground", { exact: true }).last().click();
 
@@ -135,7 +109,7 @@ test(
       timeout: 30000,
     });
 
-    await page.getByText("Run Flow", { exact: true }).click();
+    await page.getByText("Run Flow", { exact: true }).last().click();
 
     await expect(page.getByText("this is a test file")).toBeVisible({
       timeout: 3000,

src/frontend/tests/extended/integrations/duckduckgo.spec.ts

@@ -41,7 +41,10 @@ test(
           "built successfully",
         ) ?? false;
 
-      await page.getByTestId("output-inspection-data").first().click();
+      await page
+        .getByTestId("output-inspection-data-duckduckgosearch")
+        .first()
+        .click();
 
       if (isBuiltSuccessfully) {
         await page.getByRole("gridcell").first().click();

src/frontend/tests/extended/integrations/youtube-transcripts.spec.ts

@@ -35,7 +35,10 @@ test.skip(
 
     await page.waitForSelector("text=built successfully", { timeout: 3000 });
 
-    await page.getByTestId("output-inspection-transcript").first().click();
+    await page
+      .getByTestId("output-inspection-transcript-youtube-transcripts")
+      .first()
+      .click();
     await page.waitForSelector("text=Component Output", { timeout: 3000 });
     await page.getByRole("gridcell").first().click();
     const value = await page.getByPlaceholder("Empty").inputValue();