diff --git a/src/backend/base/langflow/base/models/model_input_constants.py b/src/backend/base/langflow/base/models/model_input_constants.py index 19fbd988c..6e20e6da7 100644 --- a/src/backend/base/langflow/base/models/model_input_constants.py +++ b/src/backend/base/langflow/base/models/model_input_constants.py @@ -29,10 +29,25 @@ def get_filtered_inputs(component_class): def process_inputs(component_data: Input): + """Processes and modifies an input configuration based on its type or name. + + Adjusts properties such as value, advanced status, real-time refresh, and additional information for specific + input types or names to ensure correct behavior in the UI and provider integration. + + Args: + component_data: The input configuration to process. + + Returns: + The modified input configuration. + """ if isinstance(component_data, SecretStrInput): component_data.value = "" component_data.load_from_db = False - elif component_data.name in {"temperature", "tool_model_enabled", "base_url"}: + component_data.real_time_refresh = True + elif component_data.name == "tool_model_enabled": + component_data.advanced = True + component_data.value = True + elif component_data.name in {"temperature", "base_url"}: component_data = set_advanced_true(component_data) elif component_data.name == "model_name": component_data = set_real_time_refresh_false(component_data) diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.json b/src/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.json index b0ab75c83..94fc4999d 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.json @@ -853,7 +853,21 @@ { "allows_loop": false, "cache": true, - "display_name": "Messages", + "display_name": "Message", + "group_outputs": false, + "method": "retrieve_messages_as_text", + "name": "messages_text", + "selected": "Message", + "tool_mode": true, + "types": [ + "Message" + ], + "value": "__UNDEFINED__" + }, + { + "allows_loop": false, + "cache": true, + "display_name": "Dataframe", "group_outputs": false, "method": "retrieve_messages_dataframe", "name": "dataframe", @@ -884,7 +898,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from typing import Any, cast\n\nfrom langflow.custom.custom_component.component import Component\nfrom langflow.inputs.inputs import DropdownInput, HandleInput, IntInput, MessageTextInput, MultilineInput, TabInput\nfrom langflow.memory import aget_messages, astore_message\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\nfrom langflow.template.field.base import Output\nfrom langflow.utils.component_utils import set_current_fields, set_field_display\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass MemoryComponent(Component):\n display_name = \"Message History\"\n description = \"Stores or retrieves stored chat messages from Langflow tables or an external memory.\"\n icon = \"message-square-more\"\n name = \"Memory\"\n default_keys = [\"mode\", \"memory\"]\n mode_config = {\n \"Store\": [\"message\", \"memory\", \"sender\", \"sender_name\", \"session_id\"],\n \"Retrieve\": [\"n_messages\", \"order\", \"template\", \"memory\"],\n }\n\n inputs = [\n TabInput(\n name=\"mode\",\n display_name=\"Mode\",\n options=[\"Retrieve\", \"Store\"],\n value=\"Retrieve\",\n info=\"Operation mode: Store messages or Retrieve messages.\",\n real_time_refresh=True,\n ),\n MessageTextInput(\n name=\"message\",\n display_name=\"Message\",\n info=\"The chat message to be stored.\",\n tool_mode=True,\n dynamic=True,\n show=False,\n ),\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 advanced=True,\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 show=False,\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 show=False,\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 required=True,\n show=False,\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 show=False,\n ),\n ]\n\n outputs = [Output(display_name=\"Messages\", name=\"dataframe\", method=\"retrieve_messages_dataframe\", dynamic=True)]\n\n def update_outputs(self, frontend_node: dict, field_name: str, field_value: Any) -> dict:\n \"\"\"Dynamically show only the relevant output based on the selected output type.\"\"\"\n if field_name == \"mode\":\n # Start with empty outputs\n frontend_node[\"outputs\"] = []\n if field_value == \"Store\":\n frontend_node[\"outputs\"] = [\n Output(\n display_name=\"Stored Messages\",\n name=\"stored_messages\",\n method=\"store_message\",\n hidden=True,\n dynamic=True,\n )\n ]\n if field_value == \"Retrieve\":\n frontend_node[\"outputs\"] = [\n Output(\n display_name=\"Messages\", name=\"dataframe\", method=\"retrieve_messages_dataframe\", dynamic=True\n )\n ]\n return frontend_node\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 and not hasattr(self.memory, \"aget_messages\"):\n memory_name = type(self.memory).__name__\n err_msg = f\"External Memory object ({memory_name}) must have 'aget_messages' method.\"\n raise AttributeError(err_msg)\n # Check if n_messages is None or 0\n if n_messages == 0:\n stored = []\n elif 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 cast(Data, stored)\n\n async def retrieve_messages_dataframe(self) -> DataFrame:\n \"\"\"Convert the retrieved messages into a DataFrame.\n\n Returns:\n DataFrame: A DataFrame containing the message data.\n \"\"\"\n messages = await self.retrieve_messages()\n return DataFrame(messages)\n\n async def store_message(self) -> Message:\n message = Message(text=self.message) if isinstance(self.message, str) else self.message\n\n message.session_id = self.session_id or message.session_id\n message.sender = self.sender or message.sender or MESSAGE_SENDER_AI\n message.sender_name = self.sender_name or message.sender_name or MESSAGE_SENDER_NAME_AI\n\n stored_messages: list[Message] = []\n\n if self.memory:\n self.memory.session_id = message.session_id\n lc_message = message.to_lc_message()\n await self.memory.aadd_messages([lc_message])\n\n stored_messages = await self.memory.aget_messages() or []\n\n stored_messages = [Message.from_lc_message(m) for m in stored_messages] if stored_messages else []\n\n if message.sender:\n stored_messages = [m for m in stored_messages if m.sender == message.sender]\n else:\n await astore_message(message, flow_id=self.graph.flow_id)\n stored_messages = (\n await aget_messages(\n session_id=message.session_id, sender_name=message.sender_name, sender=message.sender\n )\n or []\n )\n\n if not stored_messages:\n msg = \"No messages were stored. Please ensure that the session ID and sender are properly set.\"\n raise ValueError(msg)\n\n stored_message = stored_messages[0]\n self.status = stored_message\n return stored_message\n\n def update_build_config(\n self,\n build_config: dotdict,\n field_value: Any, # noqa: ARG002\n field_name: str | None = None, # noqa: ARG002\n ) -> dotdict:\n return set_current_fields(\n build_config=build_config,\n action_fields=self.mode_config,\n selected_action=build_config[\"mode\"][\"value\"],\n default_fields=self.default_keys,\n func=set_field_display,\n )\n" + "value": "from typing import Any, cast\n\nfrom langflow.custom.custom_component.component import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.inputs.inputs import DropdownInput, HandleInput, IntInput, MessageTextInput, MultilineInput, TabInput\nfrom langflow.memory import aget_messages, astore_message\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\nfrom langflow.template.field.base import Output\nfrom langflow.utils.component_utils import set_current_fields, set_field_display\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass MemoryComponent(Component):\n display_name = \"Message History\"\n description = \"Stores or retrieves stored chat messages from Langflow tables or an external memory.\"\n icon = \"message-square-more\"\n name = \"Memory\"\n default_keys = [\"mode\", \"memory\"]\n mode_config = {\n \"Store\": [\"message\", \"memory\", \"sender\", \"sender_name\", \"session_id\"],\n \"Retrieve\": [\"n_messages\", \"order\", \"template\", \"memory\"],\n }\n\n inputs = [\n TabInput(\n name=\"mode\",\n display_name=\"Mode\",\n options=[\"Retrieve\", \"Store\"],\n value=\"Retrieve\",\n info=\"Operation mode: Store messages or Retrieve messages.\",\n real_time_refresh=True,\n ),\n MessageTextInput(\n name=\"message\",\n display_name=\"Message\",\n info=\"The chat message to be stored.\",\n tool_mode=True,\n dynamic=True,\n show=False,\n ),\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 advanced=True,\n ),\n DropdownInput(\n name=\"sender_type\",\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\",\n display_name=\"Sender\",\n info=\"The sender of the message. Might be Machine or User. \"\n \"If empty, the current sender parameter will be used.\",\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 show=False,\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 show=False,\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 value=\"\",\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 required=True,\n ),\n MultilineInput(\n name=\"template\",\n display_name=\"Template\",\n info=\"The template to use for formatting the data. \"\n \"It can contain the keys {text}, {sender} or any other key in the message data.\",\n value=\"{sender_name}: {text}\",\n advanced=True,\n show=False,\n ),\n ]\n\n outputs = [\n Output(display_name=\"Message\", name=\"messages_text\", method=\"retrieve_messages_as_text\", dynamic=True),\n Output(display_name=\"Dataframe\", name=\"dataframe\", method=\"retrieve_messages_dataframe\", dynamic=True),\n ]\n\n def update_outputs(self, frontend_node: dict, field_name: str, field_value: Any) -> dict:\n \"\"\"Dynamically show only the relevant output based on the selected output type.\"\"\"\n if field_name == \"mode\":\n # Start with empty outputs\n frontend_node[\"outputs\"] = []\n if field_value == \"Store\":\n frontend_node[\"outputs\"] = [\n Output(\n display_name=\"Stored Messages\",\n name=\"stored_messages\",\n method=\"store_message\",\n hidden=True,\n dynamic=True,\n )\n ]\n if field_value == \"Retrieve\":\n frontend_node[\"outputs\"] = [\n Output(\n display_name=\"Messages\", name=\"messages_text\", method=\"retrieve_messages_as_text\", dynamic=True\n ),\n Output(\n display_name=\"Dataframe\", name=\"dataframe\", method=\"retrieve_messages_dataframe\", dynamic=True\n ),\n ]\n return frontend_node\n\n async def store_message(self) -> Message:\n message = Message(text=self.message) if isinstance(self.message, str) else self.message\n\n message.session_id = self.session_id or message.session_id\n message.sender = self.sender or message.sender or MESSAGE_SENDER_AI\n message.sender_name = self.sender_name or message.sender_name or MESSAGE_SENDER_NAME_AI\n\n stored_messages: list[Message] = []\n\n if self.memory:\n self.memory.session_id = message.session_id\n lc_message = message.to_lc_message()\n await self.memory.aadd_messages([lc_message])\n\n stored_messages = await self.memory.aget_messages() or []\n\n stored_messages = [Message.from_lc_message(m) for m in stored_messages] if stored_messages else []\n\n if message.sender:\n stored_messages = [m for m in stored_messages if m.sender == message.sender]\n else:\n await astore_message(message, flow_id=self.graph.flow_id)\n stored_messages = (\n await aget_messages(\n session_id=message.session_id, sender_name=message.sender_name, sender=message.sender\n )\n or []\n )\n\n if not stored_messages:\n msg = \"No messages were stored. Please ensure that the session ID and sender are properly set.\"\n raise ValueError(msg)\n\n stored_message = stored_messages[0]\n self.status = stored_message\n return stored_message\n\n async def retrieve_messages(self) -> Data:\n sender_type = self.sender_type\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_type == \"Machine and User\":\n sender_type = None\n\n if self.memory and not hasattr(self.memory, \"aget_messages\"):\n memory_name = type(self.memory).__name__\n err_msg = f\"External Memory object ({memory_name}) must have 'aget_messages' method.\"\n raise AttributeError(err_msg)\n # Check if n_messages is None or 0\n if n_messages == 0:\n stored = []\n elif 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_type:\n expected_type = MESSAGE_SENDER_AI if sender_type == 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_type,\n sender_name=sender_name,\n session_id=session_id,\n limit=n_messages,\n order=order,\n )\n self.status = stored\n return cast(Data, 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\n async def retrieve_messages_dataframe(self) -> DataFrame:\n \"\"\"Convert the retrieved messages into a DataFrame.\n\n Returns:\n DataFrame: A DataFrame containing the message data.\n \"\"\"\n messages = await self.retrieve_messages()\n return DataFrame(messages)\n\n def update_build_config(\n self,\n build_config: dotdict,\n field_value: Any, # noqa: ARG002\n field_name: str | None = None, # noqa: ARG002\n ) -> dotdict:\n return set_current_fields(\n build_config=build_config,\n action_fields=self.mode_config,\n selected_action=build_config[\"mode\"][\"value\"],\n default_fields=self.default_keys,\n func=set_field_display,\n )\n" }, "memory": { "_input_type": "HandleInput", @@ -990,9 +1004,9 @@ "_input_type": "DropdownInput", "advanced": true, "combobox": false, - "display_name": "Sender Type", + "display_name": "Sender", "dynamic": false, - "info": "Filter by sender type.", + "info": "The sender of the message. Might be Machine or User. If empty, the current sender parameter will be used.", "name": "sender", "options": [ "Machine", @@ -1030,6 +1044,31 @@ "type": "str", "value": "" }, + "sender_type": { + "_input_type": "DropdownInput", + "advanced": true, + "combobox": false, + "dialog_inputs": {}, + "display_name": "Sender Type", + "dynamic": false, + "info": "Filter by sender type.", + "name": "sender_type", + "options": [ + "Machine", + "User", + "Machine and User" + ], + "options_metadata": [], + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "toggle": false, + "tool_mode": false, + "trace_as_metadata": true, + "type": "str", + "value": "Machine and User" + }, "session_id": { "_input_type": "MessageTextInput", "advanced": true,