WORKFLOWFree BlueprintLicense: MIT
Workflow to Langflow Graph to Image Sentiment Analysis
Workflow to Langflow Graph to Image Sentiment Analysis is a verified WORKFLOW blueprint on Promptally in Make.com & Langflow Graphs optimized for leading AI models. Multi-step automated node blueprint for Langflow Graph to Image Sentiment Analysis. Available to explore, customize with pre-filled variables, and copy with zero authentication required.
Created by @promptallyTechnical Specifications
| Specification | Value |
|---|---|
| Artifact type | WORKFLOW (Make.com & Langflow Graphs) |
| Compatible engines | Universal / Model Agnostic |
| Input variables | None (pre-filled) |
| Approximate tokens | 15,973 |
| Source file | Image Sentiment Analysis.json |
| Access tier | Free public (MIT) |
Pre-Filled Template
Image Sentiment Analysis.json
{
"data": {
"edges": [
{
"animated": false,
"className": "",
"data": {
"sourceHandle": {
"dataType": "ChatInput",
"id": "ChatInput-7S2Wg",
"name": "message",
"output_types": [
"Message"
]
},
"targetHandle": {
"fieldName": "input_value",
"id": "LanguageModelComponent-KHx2J",
"inputTypes": [
"Message"
],
"type": "str"
}
},
"id": "reactflow__edge-ChatInput-7S2Wg{\u0153dataType\u0153:\u0153ChatInput\u0153,\u0153id\u0153:\u0153ChatInput-7S2Wg\u0153,\u0153name\u0153:\u0153message\u0153,\u0153output_types\u0153:[\u0153Message\u0153]}-LanguageModelComponent-KHx2J{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153LanguageModelComponent-KHx2J\u0153,\u0153inputTypes\u0153:[\u0153Message\u0153],\u0153type\u0153:\u0153str\u0153}",
"selected": false,
"source": "ChatInput-7S2Wg",
"sourceHandle": "{\u0153dataType\u0153: \u0153ChatInput\u0153, \u0153id\u0153: \u0153ChatInput-7S2Wg\u0153, \u0153name\u0153: \u0153message\u0153, \u0153output_types\u0153: [\u0153Message\u0153]}",
"target": "LanguageModelComponent-KHx2J",
"targetHandle": "{\u0153fieldName\u0153: \u0153input_value\u0153, \u0153id\u0153: \u0153LanguageModelComponent-KHx2J\u0153, \u0153inputTypes\u0153: [\u0153Message\u0153], \u0153type\u0153: \u0153str\u0153}"
},
{
"animated": false,
"className": "",
"data": {
"sourceHandle": {
"dataType": "LanguageModelComponent",
"id": "LanguageModelComponent-KHx2J",
"name": "text_output",
"output_types": [
"Message"
]
},
"targetHandle": {
"fieldName": "input_value",
"id": "ChatOutput-Ou5RJ",
"inputTypes": [
"Data",
"JSON",
"DataFrame",
"Table",
"Message"
],
"type": "str"
}
},
"id": "reactflow__edge-LanguageModelComponent-KHx2J{\u0153dataType\u0153:\u0153LanguageModelComponent\u0153,\u0153id\u0153:\u0153LanguageModelComponent-KHx2J\u0153,\u0153name\u0153:\u0153text_output\u0153,\u0153output_types\u0153:[\u0153Message\u0153]}-ChatOutput-Ou5RJ{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153ChatOutput-Ou5RJ\u0153,\u0153inputTypes\u0153:[\u0153Data\u0153,\u0153JSON\u0153,\u0153DataFrame\u0153,\u0153Table\u0153,\u0153Message\u0153],\u0153type\u0153:\u0153str\u0153}",
"selected": false,
"source": "LanguageModelComponent-KHx2J",
"sourceHandle": "{\u0153dataType\u0153: \u0153LanguageModelComponent\u0153, \u0153id\u0153: \u0153LanguageModelComponent-KHx2J\u0153, \u0153name\u0153: \u0153text_output\u0153, \u0153output_types\u0153: [\u0153Message\u0153]}",
"target": "ChatOutput-Ou5RJ",
"targetHandle": "{\u0153fieldName\u0153: \u0153input_value\u0153, \u0153id\u0153: \u0153ChatOutput-Ou5RJ\u0153, \u0153inputTypes\u0153: [\u0153Data\u0153, \u0153JSON\u0153, \u0153DataFrame\u0153, \u0153Table\u0153, \u0153Message\u0153], \u0153type\u0153: \u0153str\u0153}"
}
],
"nodes": [
{
"data": {
"description": "Get chat inputs from the Playground.",
"display_name": "Chat Input",
"id": "ChatInput-7S2Wg",
"node": {
"base_classes": [
"Message"
],
"beta": false,
"conditional_paths": [],
"custom_fields": {},
"description": "Get chat inputs from the Playground.",
"display_name": "Chat Input",
"documentation": "",
"edited": false,
"field_order": [
"input_value",
"should_store_message",
"sender",
"sender_name",
"session_id",
"context_id",
"files"
],
"frozen": false,
"icon": "MessagesSquare",
"legacy": false,
"lf_version": "1.10.0",
"metadata": {
"code_hash": "45974e6b1909",
"component_class_name": "ChatInput",
"dependencies": {
"dependencies": [
{
"name": "lfx",
"version": null
}
],
"total_dependencies": 1
},
"module": "lfx.components.input_output.chat.ChatInput"
},
"output_types": [],
"outputs": [
{
"allows_loop": false,
"cache": true,
"display_name": "Chat Message",
"group_outputs": false,
"method": "message_response",
"name": "message",
"selected": "Message",
"tool_mode": true,
"types": [
"Message"
],
"value": "__UNDEFINED__"
}
],
"pinned": false,
"template": {
"_type": "Component",
"code": {
"advanced": true,
"api_editable": false,
"dynamic": true,
"fileTypes": [],
"file_path": "",
"info": "",
"list": false,
"load_from_db": false,
"multiline": true,
"name": "code",
"password": false,
"placeholder": "",
"required": true,
"show": true,
"title_case": false,
"type": "code",
"value": "from lfx.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom lfx.base.io.chat import ChatComponent\nfrom lfx.inputs.inputs import BoolInput\nfrom lfx.io import (\n DropdownInput,\n FileInput,\n MessageTextInput,\n MultilineInput,\n Output,\n)\nfrom lfx.schema.image import get_file_paths\nfrom lfx.schema.message import Message\nfrom lfx.services.deps import get_settings_service\nfrom lfx.utils.constants import (\n MESSAGE_SENDER_AI,\n MESSAGE_SENDER_NAME_USER,\n MESSAGE_SENDER_USER,\n)\nfrom lfx.utils.file_path_security import (\n component_file_access_scopes,\n enforce_local_file_access,\n is_local_file_access_restricted,\n)\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n documentation: str = \"https://docs.langflow.org/chat-input-and-output\"\n icon = \"MessagesSquare\"\n name = \"ChatInput\"\n minimized = True\n\n inputs = [\n MultilineInput(\n name=\"input_value\",\n display_name=\"Input Text\",\n value=\"\",\n info=\"Message to be passed as input.\",\n input_types=[],\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_USER,\n info=\"Type of sender.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=MESSAGE_SENDER_NAME_USER,\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=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n FileInput(\n name=\"files\",\n display_name=\"Files\",\n file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n info=\"Files to be sent with the message.\",\n advanced=True,\n is_list=True,\n temp_file=True,\n ),\n ]\n outputs = [\n Output(display_name=\"Chat Message\", name=\"message\", method=\"message_response\"),\n ]\n\n async def message_response(self) -> Message:\n # Ensure files is a list and filter out empty/None values\n files = self.files if self.files else []\n if files and not isinstance(files, list):\n files = [files]\n # Filter out None/empty values\n files = [f for f in files if f is not None and f != \"\"]\n\n # Build endpoints inject caller-controlled file references directly into\n # ChatInput. Resolve and validate local references before the Message can\n # later read them while constructing model attachment content.\n if files:\n settings_service = get_settings_service()\n storage_type = getattr(getattr(settings_service, \"settings\", None), \"storage_type\", \"local\")\n # The storage factory falls back to local storage for unsupported\n # values, so only the explicitly configured S3 backend may bypass\n # filesystem containment.\n if str(storage_type).lower() != \"s3\" and is_local_file_access_restricted():\n scope_ids = list(component_file_access_scopes(self))\n # Public executions replace graph.flow_id with a per-visitor\n # virtual ID. Only the server-populated source provenance may\n # restore the already-validated public attachment namespace.\n source_flow_id = getattr(self.graph, \"source_flow_id\", None)\n if source_flow_id:\n scope_ids.append(source_flow_id)\n for file_path in get_file_paths(files):\n enforce_local_file_access(file_path, scope_ids=scope_ids)\n\n session_id = self.session_id or self.graph.session_id or \"\"\n message = await Message.create(\n text=self.input_value,\n sender=self.sender,\n sender_name=self.sender_name,\n session_id=session_id,\n context_id=self.context_id,\n files=files,\n )\n if session_id and isinstance(message, Message) and self.should_store_message:\n stored_message = await self.send_message(\n message,\n )\n self.message.value = stored_message\n message = stored_message\n\n self.status = message\n return message\n"
},
"context_id": {
"_input_type": "MessageTextInput",
"advanced": true,
"display_name": "Context ID",
"dynamic": false,
"info": "The context ID of the chat. Adds an extra layer to the local memory.",
"input_types": [
"Message"
],
"list": false,
"list_add_label": "Add More",
"load_from_db": false,
"name": "context_id",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"tool_mode": false,
"trace_as_input": true,
"trace_as_metadata": true,
"type": "str",
"value": ""
},
"files": {
"_input_type": "FileInput",
"advanced": true,
"display_name": "Files",
"dynamic": false,
"fileTypes": [
"csv",
"json",
"pdf",
"txt",
"md",
"mdx",
"yaml",
"yml",
"xml",
"html",
"htm",
"docx",
"py",
"sh",
"sql",
"js",
"ts",
"tsx",
"jpg",
"jpeg",
"png",
"bmp",
"image"
],
"file_path": "",
"info": "Files to be sent with the message.",
"list": true,
"name": "files",
"placeholder": "",
"required": false,
"show": true,
"temp_file": true,
"title_case": false,
"trace_as_metadata": true,
"type": "file",
"value": ""
},
"input_value": {
"_input_type": "MultilineInput",
"advanced": false,
"display_name": "Input Text",
"dynamic": false,
"info": "Message to be passed as input.",
"input_types": [],
"list": false,
"load_from_db": false,
"multiline": true,
"name": "input_value",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"tool_mode": false,
"trace_as_input": true,
"trace_as_metadata": true,
"type": "str",
"value": ""
},
"sender": {
"_input_type": "DropdownInput",
"advanced": true,
"combobox": false,
"display_name": "Sender Type",
"dynamic": false,
"info": "Type of sender.",
"name": "sender",
"options": [
"Machine",
"User"
],
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"tool_mode": false,
"trace_as_metadata": true,
"type": "str",
"value": "User"
},
"sender_name": {
"_input_type": "MessageTextInput",
"advanced": true,
"display_name": "Sender Name",
"dynamic": false,
"info": "Name of the sender.",
"input_types": [
"Message"
],
"list": false,
"load_from_db": false,
"name": "sender_name",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"tool_mode": false,
"trace_as_input": true,
"trace_as_metadata": true,
"type": "str",
"value": "User"
},
"session_id": {
"_input_type": "MessageTextInput",
"advanced": true,
"display_name": "Session ID",
"dynamic": false,
"info": "The session ID of the chat. If empty, the current session ID parameter will be used.",
"input_types": [
"Message"
],
"list": false,
"load_from_db": false,
"name": "session_id",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"tool_mode": false,
"trace_as_input": true,
"trace_as_metadata": true,
"type": "str",
"value": ""
},
"should_store_message": {
"_input_type": "BoolInput",
"advanced": true,
"display_name": "Store Messages",
"dynamic": false,
"info": "Store the message in the history.",
"list": false,
"name": "should_store_message",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"trace_as_metadata": true,
"type": "bool",
"value": true
}
},
"tool_mode": false
},
"type": "ChatInput"
},
"dragging": false,
"id": "ChatInput-7S2Wg",
"measured": {
"height": 206,
"width": 320
},
"position": {
"x": 224.9993099027423,
"y": 43.181148996598566
},
"positionAbsolute": {
"x": 1258.8272095125978,
"y": 367.0048451335054
},
"selected": false,
"type": "genericNode"
},
{
"data": {
"description": "Display a chat message in the Playground.",
"display_name": "Chat Output",
"id": "ChatOutput-Ou5RJ",
"node": {
"base_classes": [
"Message"
],
"beta": false,
"conditional_paths": [],
"custom_fields": {},
"description": "Display a chat message in the Playground.",
"display_name": "Chat Output",
"documentation": "",
"edited": false,
"field_order": [
"input_value",
"should_store_message",
"sender",
"sender_name",
"session_id",
"context_id",
"data_template",
"clean_data"
],
"frozen": false,
"icon": "MessagesSquare",
"legacy": false,
"lf_version": "1.10.0",
"metadata": {
"code_hash": "0aab1c78e1dc",
"component_class_name": "ChatOutput",
"dependencies": {
"dependencies": [
{
"name": "orjson",
"version": "3.11.9"
},
{
"name": "fastapi",
"version": "0.139.2"
},
{
"name": "lfx",
"version": null
}
],
"total_dependencies": 3
},
"module": "lfx.components.input_output.chat_output.ChatOutput"
},
"output_types": [],
"outputs": [
{
"allows_loop": false,
"cache": true,
"display_name": "Output Message",
"group_outputs": false,
"method": "message_response",
"name": "message",
"selected": "Message",
"tool_mode": true,
"types": [
"Message"
],
"value": "__UNDEFINED__"
}
],
"pinned": false,
"template": {
"_type": "Component",
"clean_data": {
"_input_type": "BoolInput",
"advanced": true,
"display_name": "Basic Clean Data",
"dynamic": false,
"info": "Whether to clean data before converting to string.",
"list": false,
"list_add_label": "Add More",
"name": "clean_data",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"tool_mode": false,
"trace_as_metadata": true,
"type": "bool",
"value": true
},
"code": {
"advanced": true,
"api_editable": false,
"dynamic": true,
"fileTypes": [],
"file_path": "",
"info": "",
"list": false,
"load_from_db": false,
"multiline": true,
"name": "code",
"password": false,
"placeholder": "",
"required": true,
"show": true,
"title_case": false,
"type": "code",
"value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\n\nfrom lfx.base.io.chat import ChatComponent\nfrom lfx.helpers.data import safe_convert\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, HandleInput, MessageTextInput\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.schema.properties import Source\nfrom lfx.template.field.base import Output\nfrom lfx.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 documentation: str = \"https://docs.langflow.org/chat-input-and-output\"\n icon = \"MessagesSquare\"\n name = \"ChatOutput\"\n minimized = True\n\n inputs = [\n HandleInput(\n name=\"input_value\",\n display_name=\"Inputs\",\n info=\"Message to be passed as output.\",\n input_types=[\"Data\", \"JSON\", \"DataFrame\", \"Table\", \"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=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\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 BoolInput(\n name=\"clean_data\",\n display_name=\"Basic Clean Data\",\n value=True,\n advanced=True,\n info=\"Whether to clean data before converting to string.\",\n ),\n ]\n outputs = [\n Output(\n display_name=\"Output 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, _, display_name, source_id = self.get_properties_from_source_component()\n\n # Create or use existing Message object\n if isinstance(self.input_value, Message) and not self.is_connected_to_chat_input():\n message = self.input_value\n # Update message properties\n message.text = text\n # Preserve existing session_id from the incoming message if it exists\n existing_session_id = message.session_id\n else:\n message = Message(text=text)\n existing_session_id = None\n\n # Set message properties\n message.sender = self.sender\n message.sender_name = self.sender_name\n # Preserve session_id from incoming message, or use component/graph session_id\n message.session_id = (\n self.session_id or existing_session_id or (self.graph.session_id if hasattr(self, \"graph\") else None) or \"\"\n )\n message.context_id = self.context_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\n # Store message if needed\n if message.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 # Set accumulated token usage from all upstream LLM vertices.\n # This must happen AFTER send_message() because streaming captures\n # usage from chunks and would overwrite accumulated totals.\n if hasattr(self, \"_vertex\") and self._vertex is not None:\n accumulated_usage = self._vertex._accumulate_upstream_token_usage() # noqa: SLF001\n if accumulated_usage:\n message.properties.usage = accumulated_usage\n if self.should_store_message and message.get_id():\n message = await self._update_stored_message(message)\n await self._send_message_event(message, id_=message.get_id())\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 clean_data: bool = getattr(self, \"clean_data\", False)\n if isinstance(self.input_value, list):\n return \"\\n\".join([safe_convert(item, clean_data=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, clean_data=clean_data)\n"
},
"context_id": {
"_input_type": "MessageTextInput",
"advanced": true,
"display_name": "Context ID",
"dynamic": false,
"info": "The context ID of the chat. Adds an extra layer to the local memory.",
"input_types": [
"Message"
],
"list": false,
"list_add_label": "Add More",
"load_from_db": false,
"name": "context_id",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"tool_mode": false,
"trace_as_input": true,
"trace_as_metadata": true,
"type": "str",
"value": ""
},
"data_template": {
"_input_type": "MessageTextInput",
"advanced": true,
"display_name": "Data Template",
"dynamic": false,
"info": "Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.",
"input_types": [
"Message"
],
"list": false,
"load_from_db": false,
"name": "data_template",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
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"sender": {
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"name": "sender",
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"conditional_paths": [],
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"description": "Runs a language model given a specified provider.",
"display_name": "Language Model",
"documentation": "https://docs.langflow.org/components-models",
"edited": false,
"field_order": [
"model",
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"input_value",
"system_message",
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],
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"icon": "brain-circuit",
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"lf_version": "1.10.0",
"metadata": {
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{
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},
"base_url_ibm_watsonx": {
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},
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"dynamic": true,
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"value": "from lfx.base.models.model import LCModelComponent\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.models_and_agents.model_selection import apply_model_overrides\nfrom lfx.field_typing.constants import LanguageModel\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, StrInput\nfrom lfx.io import IntInput, MessageInput, ModelInput, MultilineInput, SecretStrInput, SliderInput\n\n\nclass LanguageModelComponent(LCModelComponent):\n model_provider_policy_mode = \"delegate\"\n display_name = \"Language Model\"\n description = \"Runs a language model given a specified provider.\"\n documentation: str = \"https://docs.langflow.org/components-models\"\n icon = \"brain-circuit\"\n category = \"models\"\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n ),\n StrInput(\n name=\"model_name\",\n display_name=\"Model Name Override\",\n info=(\n \"Optional model name to use instead of the selected model. \"\n \"Can be set from a global variable for runtime model selection.\"\n ),\n advanced=True,\n load_from_db=False,\n ),\n StrInput(\n name=\"provider\",\n display_name=\"Provider Override\",\n info=(\n \"Optional provider to use with Model Name Override. Leave blank to use the selected model's provider.\"\n ),\n advanced=True,\n load_from_db=False,\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n required=False,\n show=True,\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n StrInput(\n name=\"ollama_base_url\",\n display_name=\"Ollama API URL\",\n info=\"Endpoint of the Ollama API (Ollama only)\",\n show=False,\n real_time_refresh=True,\n ),\n MessageInput(\n name=\"input_value\",\n display_name=\"Input\",\n info=\"The input text to send to the model\",\n ),\n MultilineInput(\n name=\"system_message\",\n display_name=\"System Message\",\n info=\"A system message that helps set the behavior of the assistant\",\n advanced=False,\n ),\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=\"Whether to stream the response\",\n value=False,\n advanced=True,\n ),\n SliderInput(\n name=\"temperature\",\n display_name=\"Temperature\",\n value=0.1,\n info=\"Controls randomness in responses\",\n range_spec=RangeSpec(min=0, max=1, step=0.01),\n advanced=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n ),\n ]\n\n def build_model(self) -> LanguageModel:\n model = apply_model_overrides(\n self.model,\n model_name=getattr(self, \"model_name\", None),\n provider=getattr(self, \"provider\", None),\n user_id=self.user_id,\n get_options=get_language_model_options,\n )\n return get_llm(\n model=model,\n user_id=self.user_id,\n api_key=self.api_key,\n temperature=self.temperature,\n stream=self.stream,\n max_tokens=getattr(self, \"max_tokens\", None),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n ollama_base_url=getattr(self, \"ollama_base_url\", None),\n )\n\n def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None):\n \"\"\"Dynamically update build config with user-filtered model options.\"\"\"\n return handle_model_input_update(self, build_config, field_value, field_name)\n"
},
"input_value": {
"_input_type": "MessageInput",
"advanced": false,
"display_name": "Input",
"dynamic": false,
"info": "The input text to send to the model",
"input_types": [
"Message"
],
"list": false,
"list_add_label": "Add More",
"load_from_db": false,
"name": "input_value",
"override_skip": false,
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"tool_mode": false,
"trace_as_input": true,
"trace_as_metadata": true,
"track_in_telemetry": false,
"type": "str",
"value": ""
},
"is_refresh": true,
"model": {
"_input_type": "ModelInput",
"advanced": false,
"display_name": "Language Model",
"dynamic": false,
"external_options": {
"fields": {
"data": {
"node": {
"display_name": "Connect other models",
"icon": "CornerDownLeft",
"name": "connect_other_models"
}
}
}
},
"info": "Select your model provider",
"input_types": [
"LanguageModel"
],
"list": false,
"list_add_label": "Add More",
"model_type": "language",
"name": "model",
"options": [
{
"category": "OpenAI",
"icon": "OpenAI",
"metadata": {
"api_key_param": "api_key",
"context_length": 128000,
"max_tokens_field_name": "max_tokens",
"model_class": "ChatOpenAI",
"model_name_param": "model",
"reasoning_models": [
"gpt-5.5-pro"
]
},
"name": "gpt-5.5-pro",
"provider": "OpenAI"
},
{
"category": "OpenAI",
"icon": "OpenAI",
"metadata": {
"api_key_param": "api_key",
"context_length": 128000,
"max_tokens_field_name": "max_tokens",
"model_class": "ChatOpenAI",
"model_name_param": "model",
"reasoning_models": [
"gpt-5.5"
]
},
"name": "gpt-5.5",
"provider": "OpenAI"
},
{
"category": "OpenAI",
"icon": "OpenAI",
"metadata": {
"api_key_param": "api_key",
"context_length": 128000,
"max_tokens_field_name": "max_tokens",
"model_class": "ChatOpenAI",
"model_name_param": "model",
"reasoning_models": [
"gpt-5.4-nano"
]
},
"name": "gpt-5.4-nano",
"provider": "OpenAI"
},
{
"category": "OpenAI",
"icon": "OpenAI",
"metadata": {
"api_key_param": "api_key",
"context_length": 128000,
"max_tokens_field_name": "max_tokens",
"model_class": "ChatOpenAI",
"model_name_param": "model",
"reasoning_models": [
"gpt-5.4-mini"
]
},
"name": "gpt-5.4-mini",
"provider": "OpenAI"
},
{
"category": "Google Generative AI",
"icon": "GoogleGenerativeAI",
"metadata": {
"api_key_param": "google_api_key",
"context_length": 128000,
"max_tokens_field_name": "max_output_tokens",
"model_class": "ChatGoogleGenerativeAIFixed",
"model_name_param": "model"
},
"name": "gemini-3.5-flash",
"provider": "Google Generative AI"
},
{
"category": "Google Generative AI",
"icon": "GoogleGenerativeAI",
"metadata": {
"api_key_param": "google_api_key",
"context_length": 128000,
"max_tokens_field_name": "max_output_tokens",
"model_class": "ChatGoogleGenerativeAIFixed",
"model_name_param": "model"
},
"name": "gemini-3.1-flash-lite",
"provider": "Google Generative AI"
},
{
"category": "Google Generative AI",
"icon": "GoogleGenerativeAI",
"metadata": {
"api_key_param": "google_api_key",
"context_length": 128000,
"max_tokens_field_name": "max_output_tokens",
"model_class": "ChatGoogleGenerativeAIFixed",
"model_name_param": "model"
},
"name": "gemma-4-31b-it",
"provider": "Google Generative AI"
},
{
"category": "Google Generative AI",
"icon": "GoogleGenerativeAI",
"metadata": {
"api_key_param": "google_api_key",
"context_length": 128000,
"max_tokens_field_name": "max_output_tokens",
"model_class": "ChatGoogleGenerativeAIFixed",
"model_name_param": "model"
},
"name": "gemma-4-26b-a4b-it",
"provider": "Google Generative AI"
}
],
"override_skip": false,
"placeholder": "Setup Provider",
"real_time_refresh": true,
"refresh_button": true,
"required": true,
"show": true,
"title_case": false,
"tool_mode": false,
"trace_as_input": true,
"track_in_telemetry": false,
"type": "model",
"value": ""
},
"model_name": {
"_input_type": "StrInput",
"advanced": true,
"display_name": "Model Name Override",
"dynamic": false,
"info": "Optional model name to use instead of the selected model. Can be set from a global variable for runtime model selection.",
"list": false,
"list_add_label": "Add More",
"load_from_db": false,
"name": "model_name",
"override_skip": false,
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"tool_mode": false,
"trace_as_metadata": true,
"track_in_telemetry": false,
"type": "str",
"value": ""
},
"ollama_base_url": {
"_input_type": "StrInput",
"advanced": false,
"display_name": "Ollama API URL",
"dynamic": false,
"info": "Endpoint of the Ollama API (Ollama only)",
"input_types": [],
"list": false,
"list_add_label": "Add More",
"load_from_db": false,
"name": "ollama_base_url",
"override_skip": false,
"placeholder": "",
"real_time_refresh": true,
"required": false,
"show": false,
"title_case": false,
"tool_mode": false,
"trace_as_input": true,
"trace_as_metadata": true,
"track_in_telemetry": false,
"type": "str",
"value": ""
},
"project_id": {
"_input_type": "StrInput",
"advanced": false,
"display_name": "watsonx Project ID",
"dynamic": false,
"info": "The project ID associated with the foundation model (IBM watsonx.ai only)",
"list": false,
"list_add_label": "Add More",
"load_from_db": false,
"name": "project_id",
"override_skip": false,
"placeholder": "",
"required": false,
"show": false,
"title_case": false,
"tool_mode": false,
"trace_as_metadata": true,
"track_in_telemetry": false,
"type": "str",
"value": ""
},
"provider": {
"_input_type": "StrInput",
"advanced": true,
"display_name": "Provider Override",
"dynamic": false,
"info": "Optional provider to use with Model Name Override. Leave blank to use the selected model's provider.",
"list": false,
"list_add_label": "Add More",
"load_from_db": false,
"name": "provider",
"override_skip": false,
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"tool_mode": false,
"trace_as_metadata": true,
"track_in_telemetry": false,
"type": "str",
"value": ""
},
"stream": {
"_input_type": "BoolInput",
"advanced": true,
"display_name": "Stream",
"dynamic": false,
"info": "Whether to stream the response",
"list": false,
"list_add_label": "Add More",
"name": "stream",
"override_skip": false,
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"tool_mode": false,
"trace_as_metadata": true,
"track_in_telemetry": true,
"type": "bool",
"value": false
},
"system_message": {
"_input_type": "MultilineInput",
"advanced": false,
"ai_enabled": false,
"copy_field": false,
"display_name": "System Message",
"dynamic": false,
"info": "A system message that helps set the behavior of the assistant",
"input_types": [
"Message"
],
"list": false,
"list_add_label": "Add More",
"load_from_db": false,
"multiline": true,
"name": "system_message",
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"title_case": false,
"tool_mode": false,
"trace_as_input": true,
"trace_as_metadata": true,
"track_in_telemetry": false,
"type": "str",
"value": "You are an image sentiment analyzer. Look at the image provided in the input and classify its overall sentiment as Positive, Negative, or Neutral, then briefly describe what the image actually shows. Judge ONLY from the image content - never invent details you cannot see. Respond with EXACTLY one line, nothing else:\nSentiment: <Positive|Negative|Neutral>, Description: <a brief description of the image>"
},
"temperature": {
"_input_type": "SliderInput",
"advanced": true,
"display_name": "Temperature",
"dynamic": false,
"info": "Controls randomness in responses",
"max_label": "",
"max_label_icon": "",
"min_label": "",
"min_label_icon": "",
"name": "temperature",
"override_skip": false,
"placeholder": "",
"range_spec": {
"max": 1,
"min": 0,
"step": 0.01,
"step_type": "float"
},
"required": false,
"show": true,
"slider_buttons": false,
"slider_buttons_options": [],
"slider_input": false,
"title_case": false,
"tool_mode": false,
"track_in_telemetry": false,
"type": "slider",
"value": 0.1
}
},
"tool_mode": false
},
"selected_output": "text_output",
"showNode": true,
"type": "LanguageModelComponent"
},
"dragging": false,
"id": "LanguageModelComponent-KHx2J",
"measured": {
"height": 370,
"width": 320
},
"position": {
"x": 600,
"y": 0
},
"selected": false,
"type": "genericNode"
},
{
"data": {
"id": "note-isa",
"node": {
"description": "# \ud83d\uddbc\ufe0f Image Sentiment Analysis\n\nUpload an image and a **Language Model** (vision) classifies its sentiment (**positive / negative / neutral**) and describes it in a single `Sentiment: ..., Description: ...` line.\n\n## Setup\n1. **Language Model** - pick a **vision-capable** model (e.g. a GPT-4o / multimodal model) and add your API key. The classification instruction lives in its **System Message** (edit there).\n2. **Chat Input** - attach the image (paperclip \ud83d\udcce) you want analyzed.\n\n## How it works\n1. **Chat Input -> Language Model** - the image is sent straight to the vision model as content (unlike an Agent, the Language Model passes images natively, so the model truly sees them).\n2. **Language Model -> Chat Output** - the model returns the sentiment + description in that single-line format.\n\n## Run\nOpen **Playground**, attach an image, and run. You can send the image on its own - no extra text needed.",
"display_name": "",
"documentation": "",
"i18n_key": "template_notes.image_sentiment_analysis.7126745f",
"template": {
"backgroundColor": "blue"
}
},
"type": "note"
},
"dragging": false,
"height": 658,
"id": "note-isa",
"measured": {
"height": 658,
"width": 556
},
"position": {
"x": -386.9035366717683,
"y": -125.44896194036946
},
"positionAbsolute": {
"x": 1131.5692052860056,
"y": 32.708500674558195
},
"resizing": false,
"selected": false,
"type": "noteNode",
"width": 556
}
],
"viewport": {
"x": 367.3299145299146,
"y": 201.66448493027437,
"zoom": 0.7333355825461089
}
},
"description": "Upload an image; a vision Language Model classifies its sentiment (positive/negative/neutral) and briefly describes it. The Language Model receives the image natively, so the model actually sees it.",
"endpoint_name": null,
"id": "c295a921-5eb1-4fec-8ddd-b4166609acdc",
"is_component": false,
"last_tested_version": "1.10.0",
"locked": false,
"name": "Image Sentiment Analysis",
"tags": [
"classification"
]
}How to use this workflow
- Review the pre-filled example and source files above.
- Copy the template or download
Image Sentiment Analysis.json, then use it in your model, IDE, or workflow tool. - Compare your result with the sample output when one is provided.
Frequently Asked Questions
How do I use Workflow to Langflow Graph to Image Sentiment Analysis?
Open the workflow on Promptally, review the pre-filled example, then copy the template or download Image Sentiment Analysis.json and paste it into your model, IDE or workflow tool. Workflow to Langflow Graph to Image Sentiment Analysis is a static blueprint with no dynamic variables. Copy it and use it as-is.
Is Workflow to Langflow Graph to Image Sentiment Analysis free?
Yes. Workflow to Langflow Graph to Image Sentiment Analysis is free to view, copy and download on Promptally without creating an account. It is published under the MIT license.
What variables does Workflow to Langflow Graph to Image Sentiment Analysis need?
Workflow to Langflow Graph to Image Sentiment Analysis is a static blueprint with no dynamic variables. Copy it and use it as-is.