ref: URL and File components with Dataframe output

[edwinjosechittilappilly]

This pull request introduces several updates across multiple components to enhance functionality, improve code maintainability, and simplify data handling. Key changes include the removal of legacy methods and outputs, the addition of new configurable options, and the refactoring of data conversion logic to use a centralized utility function.

Updates to Data Handling and Outputs:

• Removed the load_message method and its associated output from the BaseFile class, consolidating the focus on DataFrame handling (src/backend/base/langflow/base/data/base_file.py). [1] [2]
• Updated the FileComponent description to reflect its focus on loading content as a DataFrame (src/backend/base/langflow/components/data/file.py).

Enhancements to the URL Component:

• Added new configurable options like filter_text_html, continue_on_failure, check_response_status, and autoset_encoding to the URLComponent for more granular control over web scraping behavior (src/backend/base/langflow/components/data/url.py).
• Replaced IntInput for crawl depth with SliderInput and introduced constants for default values to improve usability and maintainability (src/backend/base/langflow/components/data/url.py). [1] [2]
• Refactored URL validation and loading logic for better error handling and modularity (src/backend/base/langflow/components/data/url.py).

Refactoring for Code Simplification:

• Replaced custom _safe_convert methods in multiple components with a centralized safe_convert utility function to standardize data conversion (src/backend/base/langflow/components/outputs/chat.py, src/backend/base/langflow/components/processing/parser.py). [1] [2]
• Removed unnecessary imports and legacy code from several files, streamlining the codebase (src/backend/base/langflow/components/processing/save_to_file.py, src/backend/base/langflow/components/processing/parser.py). [1] [2]

Marking Legacy Components:

• Marked DataToDataFrameComponent and MessageToDataComponent as legacy to indicate their deprecated status (src/backend/base/langflow/components/processing/data_to_dataframe.py, src/backend/base/langflow/components/processing/message_to_data.py). [1] [2]

Summary by CodeRabbit

• Refactor

  • Centralized data-to-string conversion across components by delegating to a shared helper function, simplifying internal logic and improving consistency.
  • Updated file and URL processing components to clarify descriptions, streamline outputs, and improve input handling.
  • Introduced and marked legacy status for certain processing components.
  • Enhanced test coverage and updated test selectors to align with UI and component changes.

• New Features

  • Added a component to save data or messages to files in various formats, with asynchronous saving and uploading capabilities.
  • Improved URL loading with enhanced validation, recursive crawling, and more detailed output formatting.

• Bug Fixes

  • Improved error handling and validation in file saving and URL processing components.
  • Updated tests to better reflect expected behaviors and handle edge cases.

• Chores

  • Reformatted and updated starter project configurations for clarity and consistency.
  • Adjusted test scripts to match updated component outputs and UI changes.

[coderabbitai]

Actionable comments posted: 1

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Reviewing files that changed from the base of the PR and between 86d142a and 3e42c5c.

📒 Files selected for processing (10)

• src/backend/base/langflow/components/input_output/chat_output.py (3 hunks)
• src/backend/base/langflow/initial_setup/starter_projects/blog_writer.py (3 hunks)
• src/backend/base/langflow/initial_setup/starter_projects/document_qa.py (2 hunks)
• src/backend/base/langflow/initial_setup/starter_projects/vector_store_rag.py (3 hunks)
• src/backend/tests/unit/initial_setup/starter_projects/test_vector_store_rag.py (1 hunks)
• src/frontend/tests/core/features/freeze.spec.ts (1 hunks)
• src/frontend/tests/core/features/stop-building.spec.ts (1 hunks)
• src/frontend/tests/core/unit/fileUploadComponent.spec.ts (1 hunks)
• src/frontend/tests/extended/features/loop-component.spec.ts (3 hunks)
• src/frontend/tests/extended/integrations/chatInputOutputUser-shard-1.spec.ts (6 hunks)

🚧 Files skipped from review as they are similar to previous changes (8)

• src/frontend/tests/core/features/stop-building.spec.ts
• src/frontend/tests/core/features/freeze.spec.ts
• src/backend/base/langflow/initial_setup/starter_projects/blog_writer.py
• src/backend/tests/unit/initial_setup/starter_projects/test_vector_store_rag.py
• src/backend/base/langflow/initial_setup/starter_projects/document_qa.py
• src/backend/base/langflow/initial_setup/starter_projects/vector_store_rag.py
• src/frontend/tests/extended/integrations/chatInputOutputUser-shard-1.spec.ts
• src/frontend/tests/core/unit/fileUploadComponent.spec.ts

🧰 Additional context used

🧬 Code Graph Analysis (2)

src/frontend/tests/extended/features/loop-component.spec.ts (1)

src/frontend/tests/utils/upload-file.ts (1)

• uploadFile (34-83)

src/backend/base/langflow/components/input_output/chat_output.py (2)

src/backend/base/langflow/helpers/data.py (1)

• safe_convert (165-191)

src/backend/base/langflow/schema/data.py (1)

• Data (17-249)

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🔇 Additional comments (6)

src/backend/base/langflow/components/input_output/chat_output.py (2)

8-8: LGTM: Proper import of centralized utility function.

The import of safe_convert from langflow.helpers.data aligns with the refactoring to centralize data conversion logic across components.

---

161-168: LGTM: Well-implemented data serialization method.

The _serialize_data method properly handles JSON serialization with appropriate error handling and markdown formatting. The use of orjson with pretty printing and proper encoding conversion is good practice.

src/frontend/tests/extended/features/loop-component.spec.ts (4)

4-4: LGTM: Proper import of utility function.

The import of uploadFile utility function is correctly added and will be used later in the test.

---

131-131: LGTM: Updated selector reflects backend changes.

The selector change from "handle-urlcomponent-shownode-data-right" to "handle-urlcomponent-shownode-page results-right" correctly reflects the backend URL component output renaming mentioned in the PR objectives.

---

199-199: LGTM: File upload step enhances test coverage.

The addition of the file upload step using uploadFile(page, "test_file.txt") enhances the test flow and aligns with the backend file handling improvements.

---

204-204: LGTM: Updated expectation reflects improved behavior.

The change from expecting an error to expecting a success message ("built successfully") correctly reflects the backend improvements that now allow the loop component flow to complete successfully rather than fail.

[coderabbitai]

⚠️ Potential issue

Fix inconsistent parameter usage in safe_convert calls.

There's an inconsistency in how safe_convert is called:

• Line 197: Passes clean_data=self.clean_data parameter
• Line 200: Does not pass the clean_data parameter

This inconsistency could lead to different data cleaning behavior for list vs non-list inputs.

Apply this fix to ensure consistent behavior:

-        return safe_convert(self.input_value)
+        return safe_convert(self.input_value, clean_data=self.clean_data)

📝 Committable suggestion

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Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change

	return "\n".join([safe_convert(item, clean_data=self.clean_data) for item in self.input_value])
	if isinstance(self.input_value, Generator):
	return self.input_value
	return self._safe_convert(self.input_value)
	return safe_convert(self.input_value)
	return "\n".join([safe_convert(item, clean_data=self.clean_data) for item in self.input_value])
	if isinstance(self.input_value, Generator):
	return self.input_value
	- return safe_convert(self.input_value)
	+ return safe_convert(self.input_value, clean_data=self.clean_data)

🤖 Prompt for AI Agents

In src/backend/base/langflow/components/input_output/chat_output.py lines 197 to
200, the calls to safe_convert are inconsistent regarding the clean_data
parameter; line 197 passes clean_data=self.clean_data while line 200 does not.
To fix this, modify the call on line 200 to also include
clean_data=self.clean_data so that both list and non-list inputs are processed
with consistent data cleaning behavior.

[coderabbitai]

Actionable comments posted: 2

🧹 Nitpick comments (1)

.github/workflows/typescript_test.yml (1)

223-229: Optional: Extract shard parameters into configurable variables
For better maintainability, consider defining TESTS_PER_SHARD (5) and MAX_SHARD_COUNT (40) as env variables at the top of the workflow. Then reference them in your shard calculation and bounds check:

env:
  TESTS_PER_SHARD: 5
  MAX_SHARD_COUNT: 40
# …
          SHARD_COUNT=$(( (TEST_COUNT + TESTS_PER_SHARD - 1) / TESTS_PER_SHARD ))
          if [ $SHARD_COUNT -lt 1 ]; then
            SHARD_COUNT=1
          elif [ $SHARD_COUNT -gt $MAX_SHARD_COUNT ]; then
            SHARD_COUNT=$MAX_SHARD_COUNT
          fi

This makes future adjustments clearer and avoids hard-coded numbers in the shell logic.

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📒 Files selected for processing (4)

• .github/workflows/typescript_test.yml (1 hunks)
• src/backend/base/langflow/initial_setup/starter_projects/Custom Component Maker.json (1 hunks)
• src/backend/base/langflow/initial_setup/starter_projects/Portfolio Website Code Generator.json (5 hunks)
• src/backend/base/langflow/initial_setup/starter_projects/Research Translation Loop.json (4 hunks)

🚧 Files skipped from review as they are similar to previous changes (2)

• src/backend/base/langflow/initial_setup/starter_projects/Custom Component Maker.json
• src/backend/base/langflow/initial_setup/starter_projects/Portfolio Website Code Generator.json

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🔇 Additional comments (3)

.github/workflows/typescript_test.yml (1)

223-223: Max shard count increased from 10 to 40
The change correctly updates both the comment and the conditional logic to cap shards at 40, supporting larger test suites without overwhelming the runner.

Also applies to: 227-228

src/backend/base/langflow/initial_setup/starter_projects/Research Translation Loop.json (2)

715-715: Marking the MessageToData component as legacy
Adding "legacy": true to the MessagetoData node correctly flags this component as deprecated in starter projects.

---

755-755: Legacy component code remains unchanged
The convert_message_to_data code block appears identical to its previous implementation, which is expected since this is now a legacy component. No issues detected.

[coderabbitai]

⚠️ Potential issue

Ensure clean_data is applied for single values in ChatOutput
In convert_to_string, the list branch passes clean_data=self.clean_data, but the single‐value branch calls safe_convert(self.input_value) without it. This can lead to inconsistent output cleaning. Consider:

-    return safe_convert(self.input_value)
+    return safe_convert(self.input_value, clean_data=self.clean_data)

📝 Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change

	"value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.helpers.data import safe_convert\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n MESSAGE_SENDER_AI,\n MESSAGE_SENDER_NAME_AI,\n MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"MessagesSquare\"\n name = \"ChatOutput\"\n minimized = True\n\n inputs = [\n HandleInput(\n name=\"input_value\",\n display_name=\"Text\",\n info=\"Message to be passed as output.\",\n input_types=[\"Data\", \"DataFrame\", \"Message\"],\n required=True,\n ),\n BoolInput(\n name=\"should_store_message\",\n display_name=\"Store Messages\",\n info=\"Store the message in the history.\",\n value=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n value=MESSAGE_SENDER_AI,\n advanced=True,\n info=\"Type of sender.\",\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=MESSAGE_SENDER_NAME_AI,\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 MessageTextInput(\n name=\"data_template\",\n display_name=\"Data Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n ),\n MessageTextInput(\n name=\"background_color\",\n display_name=\"Background Color\",\n info=\"The background color of the icon.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"chat_icon\",\n display_name=\"Icon\",\n info=\"The icon of the message.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"text_color\",\n display_name=\"Text Color\",\n info=\"The text color of the name\",\n advanced=True,\n ),\n BoolInput(\n name=\"clean_data\",\n display_name=\"Basic Clean Data\",\n value=True,\n info=\"Whether to clean the data\",\n advanced=True,\n ),\n ]\n outputs = [\n Output(\n display_name=\"Message\",\n name=\"message\",\n method=\"message_response\",\n ),\n ]\n\n def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n source_dict = {}\n if id_:\n source_dict[\"id\"] = id_\n if display_name:\n source_dict[\"display_name\"] = display_name\n if source:\n # Handle case where source is a ChatOpenAI object\n if hasattr(source, \"model_name\"):\n source_dict[\"source\"] = source.model_name\n elif hasattr(source, \"model\"):\n source_dict[\"source\"] = str(source.model)\n else:\n source_dict[\"source\"] = str(source)\n return Source(**source_dict)\n\n async def message_response(self) -> Message:\n # First convert the input to string if needed\n text = self.convert_to_string()\n\n # Get source properties\n source, icon, display_name, source_id = self.get_properties_from_source_component()\n background_color = self.background_color\n text_color = self.text_color\n if self.chat_icon:\n icon = self.chat_icon\n\n # Create or use existing Message object\n if isinstance(self.input_value, Message):\n message = self.input_value\n # Update message properties\n message.text = text\n else:\n message = Message(text=text)\n\n # Set message properties\n message.sender = self.sender\n message.sender_name = self.sender_name\n message.session_id = self.session_id\n message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n message.properties.source = self._build_source(source_id, display_name, source)\n message.properties.icon = icon\n message.properties.background_color = background_color\n message.properties.text_color = text_color\n\n # Store message if needed\n if self.session_id and self.should_store_message:\n stored_message = await self.send_message(message)\n self.message.value = stored_message\n message = stored_message\n\n self.status = message\n return message\n\n def _serialize_data(self, data: Data) -> str:\n \"\"\"Serialize Data object to JSON string.\"\"\"\n # Convert data.data to JSON-serializable format\n serializable_data = jsonable_encoder(data.data)\n # Serialize with orjson, enabling pretty printing with indentation\n json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n # Convert bytes to string and wrap in Markdown code blocks\n return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\n\n def _validate_input(self) -> None:\n \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n if self.input_value is None:\n msg = \"Input data cannot be None\"\n raise ValueError(msg)\n if isinstance(self.input_value, list) and not all(\n isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n ):\n invalid_types = [\n type(item).__name__\n for item in self.input_value\n if not isinstance(item, Message | Data | DataFrame | str)\n ]\n msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n raise TypeError(msg)\n if not isinstance(\n self.input_value,\n Message | Data | DataFrame | str | list | Generator | type(None),\n ):\n type_name = type(self.input_value).__name__\n msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n raise TypeError(msg)\n\n def convert_to_string(self) -> str | Generator[Any, None, None]:\n \"\"\"Convert input data to string with proper error handling.\"\"\"\n self._validate_input()\n if isinstance(self.input_value, list):\n return \"\\n\".join([safe_convert(item, clean_data=self.clean_data) for item in self.input_value])\n if isinstance(self.input_value, Generator):\n return self.input_value\n return safe_convert(self.input_value)\n"
	def convert_to_string(self) -> str | Generator[Any, None, None]:
	"""Convert input data to string with proper error handling."""
	self._validate_input()
	if isinstance(self.input_value, list):
	return "\n".join(
	[safe_convert(item, clean_data=self.clean_data) for item in self.input_value]
	)
	if isinstance(self.input_value, Generator):
	return self.input_value
	return safe_convert(self.input_value, clean_data=self.clean_data)

🤖 Prompt for AI Agents

In src/backend/base/langflow/initial_setup/starter_projects/Research Translation
Loop.json at line 929, the convert_to_string method applies the clean_data flag
when input_value is a list but omits it for single values. To fix this, modify
the single-value branch to call safe_convert with clean_data=self.clean_data to
ensure consistent data cleaning across all input types.

[coderabbitai]

⚠️ Potential issue

Consistent clean_data handling in ParserComponent’s converter
The non‐list branch of convert_to_string omits the clean_data flag, while the list branch uses self.clean_data. For uniform behavior, update:

-        else:
-            result = safe_convert(self.input_data or False)
+        else:
+            result = safe_convert(self.input_data or False, clean_data=self.clean_data or False)

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Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change

	"value": "from langflow.custom import Component\nfrom langflow.helpers.data import safe_convert\nfrom langflow.io import (\n BoolInput,\n HandleInput,\n MessageTextInput,\n MultilineInput,\n Output,\n TabInput,\n)\nfrom langflow.schema import Data, DataFrame\nfrom langflow.schema.message import Message\n\n\nclass ParserComponent(Component):\n display_name = \"Parser\"\n description = (\n \"Format a DataFrame or Data object into text using a template. \"\n \"Enable 'Stringify' to convert input into a readable string instead.\"\n )\n icon = \"braces\"\n\n inputs = [\n TabInput(\n name=\"mode\",\n display_name=\"Mode\",\n options=[\"Parser\", \"Stringify\"],\n value=\"Parser\",\n info=\"Convert into raw string instead of using a template.\",\n real_time_refresh=True,\n ),\n MultilineInput(\n name=\"pattern\",\n display_name=\"Template\",\n info=(\n \"Use variables within curly brackets to extract column values for DataFrames \"\n \"or key values for Data.\"\n \"For example: `Name: {Name}, Age: {Age}, Country: {Country}`\"\n ),\n value=\"Text: {text}\", # Example default\n dynamic=True,\n show=True,\n required=True,\n ),\n HandleInput(\n name=\"input_data\",\n display_name=\"Data or DataFrame\",\n input_types=[\"DataFrame\", \"Data\"],\n info=\"Accepts either a DataFrame or a Data object.\",\n required=True,\n ),\n MessageTextInput(\n name=\"sep\",\n display_name=\"Separator\",\n advanced=True,\n value=\"\\n\",\n info=\"String used to separate rows/items.\",\n ),\n ]\n\n outputs = [\n Output(\n display_name=\"Parsed Text\",\n name=\"parsed_text\",\n info=\"Formatted text output.\",\n method=\"parse_combined_text\",\n ),\n ]\n\n def update_build_config(self, build_config, field_value, field_name=None):\n \"\"\"Dynamically hide/show `template` and enforce requirement based on `stringify`.\"\"\"\n if field_name == \"mode\":\n build_config[\"pattern\"][\"show\"] = self.mode == \"Parser\"\n build_config[\"pattern\"][\"required\"] = self.mode == \"Parser\"\n if field_value:\n clean_data = BoolInput(\n name=\"clean_data\",\n display_name=\"Clean Data\",\n info=(\n \"Enable to clean the data by removing empty rows and lines \"\n \"in each cell of the DataFrame/ Data object.\"\n ),\n value=True,\n advanced=True,\n required=False,\n )\n build_config[\"clean_data\"] = clean_data.to_dict()\n else:\n build_config.pop(\"clean_data\", None)\n\n return build_config\n\n def _clean_args(self):\n \"\"\"Prepare arguments based on input type.\"\"\"\n input_data = self.input_data\n\n match input_data:\n case list() if all(isinstance(item, Data) for item in input_data):\n msg = \"List of Data objects is not supported.\"\n raise ValueError(msg)\n case DataFrame():\n return input_data, None\n case Data():\n return None, input_data\n case dict() if \"data\" in input_data:\n try:\n if \"columns\" in input_data: # Likely a DataFrame\n return DataFrame.from_dict(input_data), None\n # Likely a Data object\n return None, Data(**input_data)\n except (TypeError, ValueError, KeyError) as e:\n msg = f\"Invalid structured input provided: {e!s}\"\n raise ValueError(msg) from e\n case _:\n msg = f\"Unsupported input type: {type(input_data)}. Expected DataFrame or Data.\"\n raise ValueError(msg)\n\n def parse_combined_text(self) -> Message:\n \"\"\"Parse all rows/items into a single text or convert input to string if `stringify` is enabled.\"\"\"\n # Early return for stringify option\n if self.mode == \"Stringify\":\n return self.convert_to_string()\n\n df, data = self._clean_args()\n\n lines = []\n if df is not None:\n for _, row in df.iterrows():\n formatted_text = self.pattern.format(**row.to_dict())\n lines.append(formatted_text)\n elif data is not None:\n formatted_text = self.pattern.format(**data.data)\n lines.append(formatted_text)\n\n combined_text = self.sep.join(lines)\n self.status = combined_text\n return Message(text=combined_text)\n\n def convert_to_string(self) -> Message:\n \"\"\"Convert input data to string with proper error handling.\"\"\"\n result = \"\"\n if isinstance(self.input_data, list):\n result = \"\\n\".join([safe_convert(item, clean_data=self.clean_data or False) for item in self.input_data])\n else:\n result = safe_convert(self.input_data or False)\n self.log(f\"Converted to string with length: {len(result)}\")\n\n message = Message(text=result)\n self.status = message\n return message\n"
	def convert_to_string(self) -> Message:
	"""Convert input data to string with proper error handling."""
	result = ""
	if isinstance(self.input_data, list):
	result = "\n".join([safe_convert(item, clean_data=self.clean_data or False)
	for item in self.input_data])
	else:
	result = safe_convert(self.input_data or False,
	clean_data=self.clean_data or False)
	self.log(f"Converted to string with length: {len(result)}")
	message = Message(text=result)
	self.status = message
	return message

🤖 Prompt for AI Agents

In src/backend/base/langflow/initial_setup/starter_projects/Research Translation
Loop.json at line 1536, the convert_to_string method inconsistently handles the
clean_data flag: the list branch uses self.clean_data but the non-list branch
does not. To fix this, modify the non-list branch to also pass
clean_data=self.clean_data or False to the safe_convert call, ensuring
consistent behavior for both branches.