diff --git a/src/backend/base/langflow/components/agents/agent.py b/src/backend/base/langflow/components/agents/agent.py index 841e38d31..1f2d97a0c 100644 --- a/src/backend/base/langflow/components/agents/agent.py +++ b/src/backend/base/langflow/components/agents/agent.py @@ -1,8 +1,11 @@ +from langchain_core.tools import StructuredTool + from langflow.base.agents.agent import LCToolsAgentComponent from langflow.base.models.model_input_constants import ALL_PROVIDER_FIELDS, MODEL_PROVIDERS_DICT from langflow.components.agents.tool_calling import ToolCallingAgentComponent +from langflow.components.helpers import CurrentDateComponent from langflow.components.helpers.memory import MemoryComponent -from langflow.io import DropdownInput, MultilineInput, Output +from langflow.io import BoolInput, DropdownInput, MultilineInput, Output from langflow.schema.dotdict import dotdict from langflow.schema.message import Message @@ -41,6 +44,13 @@ class AgentComponent(ToolCallingAgentComponent): ), *LCToolsAgentComponent._base_inputs, *memory_inputs, + BoolInput( + name="add_current_date_tool", + display_name="Add tool Current Date", + advanced=True, + info="If true, will add a tool to the agent that returns the current date.", + value=True, + ), ] outputs = [Output(name="response", display_name="Response", method="message_response")] @@ -50,6 +60,18 @@ class AgentComponent(ToolCallingAgentComponent): msg = "No language model selected" raise ValueError(msg) self.chat_history = self.get_memory_data() + + if self.add_current_date_tool: + if not isinstance(self.tools, list): # type: ignore[has-type] + self.tools = [] + # Convert CurrentDateComponent to a StructuredTool + current_date_tool = CurrentDateComponent().to_toolkit()[0] + if isinstance(current_date_tool, StructuredTool): + self.tools.append(current_date_tool) + else: + msg = "CurrentDateComponent must be converted to a StructuredTool" + raise ValueError(msg) + if not self.tools: msg = "Tools are required to run the agent." raise ValueError(msg) @@ -150,7 +172,19 @@ class AgentComponent(ToolCallingAgentComponent): build_config = self.update_input_types(build_config) # Validate required keys - default_keys = ["code", "_type", "agent_llm", "tools", "input_value"] + default_keys = [ + "code", + "_type", + "agent_llm", + "tools", + "input_value", + "add_current_date_tool", + "system_prompt", + "agent_description", + "max_iterations", + "handle_parsing_errors", + "verbose", + ] missing_keys = [key for key in default_keys if key not in build_config] if missing_keys: msg = f"Missing required keys in build_config: {missing_keys}" diff --git a/src/backend/base/langflow/components/custom_component/custom_component.py b/src/backend/base/langflow/components/custom_component/custom_component.py index 1425fe5f4..686ef0128 100644 --- a/src/backend/base/langflow/components/custom_component/custom_component.py +++ b/src/backend/base/langflow/components/custom_component/custom_component.py @@ -12,7 +12,13 @@ class CustomComponent(Component): name = "CustomComponent" inputs = [ - MessageTextInput(name="input_value", display_name="Input Value", value="Hello, World!", tool_mode=True), + MessageTextInput( + name="input_value", + display_name="Input Value", + info="This is a custom component Input", + value="Hello, World!", + tool_mode=True, + ), ] outputs = [ diff --git a/src/backend/base/langflow/components/data/api_request.py b/src/backend/base/langflow/components/data/api_request.py index f28660b73..f5f1aa521 100644 --- a/src/backend/base/langflow/components/data/api_request.py +++ b/src/backend/base/langflow/components/data/api_request.py @@ -38,6 +38,8 @@ class APIRequestComponent(Component): "This will fill in the dictionary fields for headers and body.", advanced=False, refresh_button=True, + real_time_refresh=True, + tool_mode=True, ), DropdownInput( name="method", @@ -63,6 +65,7 @@ class APIRequestComponent(Component): name="query_params", display_name="Query Parameters", info="The query parameters to append to the URL.", + tool_mode=True, ), IntInput( name="timeout", @@ -176,7 +179,11 @@ class APIRequestComponent(Component): headers = self.headers or {} body = self.body or {} timeout = self.timeout - query_params = self.query_params.data if self.query_params else {} + + if isinstance(self.query_params, str): + query_params = dict(parse_qsl(self.query_params)) + else: + query_params = self.query_params.data if self.query_params else {} if curl: self._build_config = self.parse_curl(curl, dotdict()) diff --git a/src/backend/base/langflow/components/data/url.py b/src/backend/base/langflow/components/data/url.py index c4967f692..08dc08f48 100644 --- a/src/backend/base/langflow/components/data/url.py +++ b/src/backend/base/langflow/components/data/url.py @@ -21,6 +21,7 @@ class URLComponent(Component): display_name="URLs", info="Enter one or more URLs, by clicking the '+' button.", is_list=True, + tool_mode=True, ), DropdownInput( name="format", diff --git a/src/backend/base/langflow/components/helpers/current_date.py b/src/backend/base/langflow/components/helpers/current_date.py index 5967a466b..0ec7b7cc1 100644 --- a/src/backend/base/langflow/components/helpers/current_date.py +++ b/src/backend/base/langflow/components/helpers/current_date.py @@ -56,6 +56,7 @@ class CurrentDateComponent(Component): ], value="UTC", info="Select the timezone for the current date and time.", + tool_mode=True, ), ] outputs = [ diff --git a/src/backend/base/langflow/components/tools/calculator.py b/src/backend/base/langflow/components/tools/calculator.py index c1c8e26ec..ffe0ea00b 100644 --- a/src/backend/base/langflow/components/tools/calculator.py +++ b/src/backend/base/langflow/components/tools/calculator.py @@ -23,6 +23,7 @@ class CalculatorToolComponent(LCToolComponent): name="expression", display_name="Expression", info="The arithmetic expression to evaluate (e.g., '4*4*(33/22)+12-20').", + tool_mode=True, ), ] diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Agent Flow.json b/src/backend/base/langflow/initial_setup/starter_projects/Agent Flow.json index 8ed3abe19..2714bf669 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Agent Flow.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Agent Flow.json @@ -737,7 +737,7 @@ "show": true, "title_case": false, "type": "code", - "value": "import ast\nimport operator\n\nfrom langchain.tools import StructuredTool\nfrom langchain_core.tools import ToolException\nfrom loguru import logger\nfrom pydantic import BaseModel, Field\n\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.field_typing import Tool\nfrom langflow.inputs import MessageTextInput\nfrom langflow.schema import Data\n\n\nclass CalculatorToolComponent(LCToolComponent):\n display_name = \"Calculator\"\n description = \"Perform basic arithmetic operations on a given expression.\"\n icon = \"calculator\"\n name = \"CalculatorTool\"\n\n inputs = [\n MessageTextInput(\n name=\"expression\",\n display_name=\"Expression\",\n info=\"The arithmetic expression to evaluate (e.g., '4*4*(33/22)+12-20').\",\n ),\n ]\n\n class CalculatorToolSchema(BaseModel):\n expression: str = Field(..., description=\"The arithmetic expression to evaluate.\")\n\n def run_model(self) -> list[Data]:\n return self._evaluate_expression(self.expression)\n\n def build_tool(self) -> Tool:\n return StructuredTool.from_function(\n name=\"calculator\",\n description=\"Evaluate basic arithmetic expressions. Input should be a string containing the expression.\",\n func=self._eval_expr_with_error,\n args_schema=self.CalculatorToolSchema,\n )\n\n def _eval_expr(self, node):\n # Define the allowed operators\n operators = {\n ast.Add: operator.add,\n ast.Sub: operator.sub,\n ast.Mult: operator.mul,\n ast.Div: operator.truediv,\n ast.Pow: operator.pow,\n }\n if isinstance(node, ast.Num):\n return node.n\n if isinstance(node, ast.BinOp):\n return operators[type(node.op)](self._eval_expr(node.left), self._eval_expr(node.right))\n if isinstance(node, ast.UnaryOp):\n return operators[type(node.op)](self._eval_expr(node.operand))\n if isinstance(node, ast.Call):\n msg = (\n \"Function calls like sqrt(), sin(), cos() etc. are not supported. \"\n \"Only basic arithmetic operations (+, -, *, /, **) are allowed.\"\n )\n raise TypeError(msg)\n msg = f\"Unsupported operation or expression type: {type(node).__name__}\"\n raise TypeError(msg)\n\n def _eval_expr_with_error(self, expression: str) -> list[Data]:\n try:\n return self._evaluate_expression(expression)\n except Exception as e:\n raise ToolException(str(e)) from e\n\n def _evaluate_expression(self, expression: str) -> list[Data]:\n try:\n # Parse the expression and evaluate it\n tree = ast.parse(expression, mode=\"eval\")\n result = self._eval_expr(tree.body)\n\n # Format the result to a reasonable number of decimal places\n formatted_result = f\"{result:.6f}\".rstrip(\"0\").rstrip(\".\")\n\n self.status = formatted_result\n return [Data(data={\"result\": formatted_result})]\n\n except (SyntaxError, TypeError, KeyError) as e:\n error_message = f\"Invalid expression: {e}\"\n self.status = error_message\n return [Data(data={\"error\": error_message, \"input\": expression})]\n except ZeroDivisionError:\n error_message = \"Error: Division by zero\"\n self.status = error_message\n return [Data(data={\"error\": error_message, \"input\": expression})]\n except Exception as e: # noqa: BLE001\n logger.opt(exception=True).debug(\"Error evaluating expression\")\n error_message = f\"Error: {e}\"\n self.status = error_message\n return [Data(data={\"error\": error_message, \"input\": expression})]\n" + "value": "import ast\nimport operator\n\nfrom langchain.tools import StructuredTool\nfrom langchain_core.tools import ToolException\nfrom loguru import logger\nfrom pydantic import BaseModel, Field\n\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.field_typing import Tool\nfrom langflow.inputs import MessageTextInput\nfrom langflow.schema import Data\n\n\nclass CalculatorToolComponent(LCToolComponent):\n display_name = \"Calculator\"\n description = \"Perform basic arithmetic operations on a given expression.\"\n icon = \"calculator\"\n name = \"CalculatorTool\"\n\n inputs = [\n MessageTextInput(\n name=\"expression\",\n display_name=\"Expression\",\n info=\"The arithmetic expression to evaluate (e.g., '4*4*(33/22)+12-20').\",\n tool_mode=True,\n ),\n ]\n\n class CalculatorToolSchema(BaseModel):\n expression: str = Field(..., description=\"The arithmetic expression to evaluate.\")\n\n def run_model(self) -> list[Data]:\n return self._evaluate_expression(self.expression)\n\n def build_tool(self) -> Tool:\n return StructuredTool.from_function(\n name=\"calculator\",\n description=\"Evaluate basic arithmetic expressions. Input should be a string containing the expression.\",\n func=self._eval_expr_with_error,\n args_schema=self.CalculatorToolSchema,\n )\n\n def _eval_expr(self, node):\n # Define the allowed operators\n operators = {\n ast.Add: operator.add,\n ast.Sub: operator.sub,\n ast.Mult: operator.mul,\n ast.Div: operator.truediv,\n ast.Pow: operator.pow,\n }\n if isinstance(node, ast.Num):\n return node.n\n if isinstance(node, ast.BinOp):\n return operators[type(node.op)](self._eval_expr(node.left), self._eval_expr(node.right))\n if isinstance(node, ast.UnaryOp):\n return operators[type(node.op)](self._eval_expr(node.operand))\n if isinstance(node, ast.Call):\n msg = (\n \"Function calls like sqrt(), sin(), cos() etc. are not supported. \"\n \"Only basic arithmetic operations (+, -, *, /, **) are allowed.\"\n )\n raise TypeError(msg)\n msg = f\"Unsupported operation or expression type: {type(node).__name__}\"\n raise TypeError(msg)\n\n def _eval_expr_with_error(self, expression: str) -> list[Data]:\n try:\n return self._evaluate_expression(expression)\n except Exception as e:\n raise ToolException(str(e)) from e\n\n def _evaluate_expression(self, expression: str) -> list[Data]:\n try:\n # Parse the expression and evaluate it\n tree = ast.parse(expression, mode=\"eval\")\n result = self._eval_expr(tree.body)\n\n # Format the result to a reasonable number of decimal places\n formatted_result = f\"{result:.6f}\".rstrip(\"0\").rstrip(\".\")\n\n self.status = formatted_result\n return [Data(data={\"result\": formatted_result})]\n\n except (SyntaxError, TypeError, KeyError) as e:\n error_message = f\"Invalid expression: {e}\"\n self.status = error_message\n return [Data(data={\"error\": error_message, \"input\": expression})]\n except ZeroDivisionError:\n error_message = \"Error: Division by zero\"\n self.status = error_message\n return [Data(data={\"error\": error_message, \"input\": expression})]\n except Exception as e: # noqa: BLE001\n logger.opt(exception=True).debug(\"Error evaluating expression\")\n error_message = f\"Error: {e}\"\n self.status = error_message\n return [Data(data={\"error\": error_message, \"input\": expression})]\n" }, "expression": { "_input_type": "MessageTextInput", @@ -988,6 +988,22 @@ "pinned": false, "template": { "_type": "Component", + "add_current_date_tool": { + "_input_type": "BoolInput", + "advanced": true, + "display_name": "Add tool Current Date", + "dynamic": false, + "info": "If true, will add a tool to the agent that returns the current date.", + "list": false, + "name": "add_current_date_tool", + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "trace_as_metadata": true, + "type": "bool", + "value": true + }, "agent_description": { "_input_type": "MultilineInput", "advanced": true, @@ -1073,7 +1089,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import ALL_PROVIDER_FIELDS, MODEL_PROVIDERS_DICT\nfrom langflow.components.agents.tool_calling import ToolCallingAgentComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.io import DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n icon = \"bot\"\n beta = True\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n DropdownInput(\n name=\"agent_llm\",\n display_name=\"Model Provider\",\n options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n value=\"OpenAI\",\n real_time_refresh=True,\n refresh_button=True,\n input_types=[],\n ),\n *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=\"Initial instructions and context provided to guide the agent's behavior.\",\n value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n advanced=False,\n ),\n *LCToolsAgentComponent._base_inputs,\n *memory_inputs,\n ]\n outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n async def message_response(self) -> Message:\n llm_model = self.get_llm()\n if llm_model is None:\n msg = \"No language model selected\"\n raise ValueError(msg)\n self.chat_history = self.get_memory_data()\n if not self.tools:\n msg = \"Tools are required to run the agent.\"\n raise ValueError(msg)\n self.set(\n llm=llm_model,\n tools=self.tools,\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self.system_prompt,\n )\n agent = self.create_agent_runnable()\n return await self.run_agent(agent)\n\n def get_memory_data(self):\n memory_kwargs = {\n component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n }\n\n return MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n def get_llm(self):\n if isinstance(self.agent_llm, str):\n try:\n provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n if provider_info:\n component_class = provider_info.get(\"component_class\")\n inputs = provider_info.get(\"inputs\")\n prefix = provider_info.get(\"prefix\", \"\")\n return self._build_llm_model(component_class, inputs, prefix)\n except Exception as e:\n msg = f\"Error building {self.agent_llm} language model\"\n raise ValueError(msg) from e\n return self.agent_llm\n\n def _build_llm_model(self, component, inputs, prefix=\"\"):\n model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n return component.set(**model_kwargs).build_model()\n\n def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n \"\"\"Delete specified fields from build_config.\"\"\"\n for field in fields:\n build_config.pop(field, None)\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n def update_build_config(self, build_config: dotdict, field_value: str, field_name: str | None = None) -> dotdict:\n if field_name == \"agent_llm\":\n # Define provider configurations as (fields_to_add, fields_to_delete)\n provider_configs: dict[str, tuple[dict, list[dict]]] = {\n provider: (\n MODEL_PROVIDERS_DICT[provider][\"fields\"],\n [\n MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n for other_provider in MODEL_PROVIDERS_DICT\n if other_provider != provider\n ],\n )\n for provider in MODEL_PROVIDERS_DICT\n }\n\n if field_value in provider_configs:\n fields_to_add, fields_to_delete = provider_configs[field_value]\n\n # Delete fields from other providers\n for fields in fields_to_delete:\n self.delete_fields(build_config, fields)\n\n # Add provider-specific fields\n if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n build_config.update(fields_to_add)\n else:\n build_config.update(fields_to_add)\n # Reset input types for agent_llm\n build_config[\"agent_llm\"][\"input_types\"] = []\n elif field_value == \"Custom\":\n # Delete all provider fields\n self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n # Update with custom component\n custom_component = DropdownInput(\n name=\"agent_llm\",\n display_name=\"Language Model\",\n options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n value=\"Custom\",\n real_time_refresh=True,\n input_types=[\"LanguageModel\"],\n )\n build_config.update({\"agent_llm\": custom_component.to_dict()})\n\n # Update input types for all fields\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\"code\", \"_type\", \"agent_llm\", \"tools\", \"input_value\"]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n\n return build_config\n" + "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import ALL_PROVIDER_FIELDS, MODEL_PROVIDERS_DICT\nfrom langflow.components.agents.tool_calling import ToolCallingAgentComponent\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n icon = \"bot\"\n beta = True\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n DropdownInput(\n name=\"agent_llm\",\n display_name=\"Model Provider\",\n options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n value=\"OpenAI\",\n real_time_refresh=True,\n refresh_button=True,\n input_types=[],\n ),\n *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=\"Initial instructions and context provided to guide the agent's behavior.\",\n value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n advanced=False,\n ),\n *LCToolsAgentComponent._base_inputs,\n *memory_inputs,\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Add tool Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n ]\n outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n async def message_response(self) -> Message:\n llm_model = self.get_llm()\n if llm_model is None:\n msg = \"No language model selected\"\n raise ValueError(msg)\n self.chat_history = self.get_memory_data()\n\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n # Convert CurrentDateComponent to a StructuredTool\n current_date_tool = CurrentDateComponent().to_toolkit()[0]\n if isinstance(current_date_tool, StructuredTool):\n self.tools.append(current_date_tool)\n else:\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise ValueError(msg)\n\n if not self.tools:\n msg = \"Tools are required to run the agent.\"\n raise ValueError(msg)\n self.set(\n llm=llm_model,\n tools=self.tools,\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self.system_prompt,\n )\n agent = self.create_agent_runnable()\n return await self.run_agent(agent)\n\n def get_memory_data(self):\n memory_kwargs = {\n component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n }\n\n return MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n def get_llm(self):\n if isinstance(self.agent_llm, str):\n try:\n provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n if provider_info:\n component_class = provider_info.get(\"component_class\")\n inputs = provider_info.get(\"inputs\")\n prefix = provider_info.get(\"prefix\", \"\")\n return self._build_llm_model(component_class, inputs, prefix)\n except Exception as e:\n msg = f\"Error building {self.agent_llm} language model\"\n raise ValueError(msg) from e\n return self.agent_llm\n\n def _build_llm_model(self, component, inputs, prefix=\"\"):\n model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n return component.set(**model_kwargs).build_model()\n\n def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n \"\"\"Delete specified fields from build_config.\"\"\"\n for field in fields:\n build_config.pop(field, None)\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n def update_build_config(self, build_config: dotdict, field_value: str, field_name: str | None = None) -> dotdict:\n if field_name == \"agent_llm\":\n # Define provider configurations as (fields_to_add, fields_to_delete)\n provider_configs: dict[str, tuple[dict, list[dict]]] = {\n provider: (\n MODEL_PROVIDERS_DICT[provider][\"fields\"],\n [\n MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n for other_provider in MODEL_PROVIDERS_DICT\n if other_provider != provider\n ],\n )\n for provider in MODEL_PROVIDERS_DICT\n }\n\n if field_value in provider_configs:\n fields_to_add, fields_to_delete = provider_configs[field_value]\n\n # Delete fields from other providers\n for fields in fields_to_delete:\n self.delete_fields(build_config, fields)\n\n # Add provider-specific fields\n if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n build_config.update(fields_to_add)\n else:\n build_config.update(fields_to_add)\n # Reset input types for agent_llm\n build_config[\"agent_llm\"][\"input_types\"] = []\n elif field_value == \"Custom\":\n # Delete all provider fields\n self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n # Update with custom component\n custom_component = DropdownInput(\n name=\"agent_llm\",\n display_name=\"Language Model\",\n options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n value=\"Custom\",\n real_time_refresh=True,\n input_types=[\"LanguageModel\"],\n )\n build_config.update({\"agent_llm\": custom_component.to_dict()})\n\n # Update input types for all fields\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"agent_llm\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n\n return build_config\n" }, "handle_parsing_errors": { "_input_type": "BoolInput", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Blog Writer.json b/src/backend/base/langflow/initial_setup/starter_projects/Blog Writer.json index 9d3f21777..21bea235a 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Blog Writer.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Blog Writer.json @@ -202,7 +202,7 @@ "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.message import Message\n\n\nclass URLComponent(Component):\n display_name = \"URL\"\n description = \"Fetch content from one or more URLs.\"\n icon = \"layout-template\"\n name = \"URL\"\n\n inputs = [\n MessageTextInput(\n name=\"urls\",\n display_name=\"URLs\",\n info=\"Enter one or more URLs, by clicking the '+' button.\",\n is_list=True,\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 ]\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" + "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.message import Message\n\n\nclass URLComponent(Component):\n display_name = \"URL\"\n description = \"Fetch content from one or more URLs.\"\n icon = \"layout-template\"\n name = \"URL\"\n\n inputs = [\n MessageTextInput(\n name=\"urls\",\n display_name=\"URLs\",\n info=\"Enter one or more URLs, by clicking the '+' button.\",\n is_list=True,\n tool_mode=True,\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 ]\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" }, "format": { "_input_type": "DropdownInput", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.json b/src/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.json index 0e2b3185f..471b6a0ef 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.json @@ -2475,7 +2475,7 @@ "show": true, "title_case": false, "type": "code", - "value": "import ast\nimport operator\n\nfrom langchain.tools import StructuredTool\nfrom langchain_core.tools import ToolException\nfrom loguru import logger\nfrom pydantic import BaseModel, Field\n\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.field_typing import Tool\nfrom langflow.inputs import MessageTextInput\nfrom langflow.schema import Data\n\n\nclass CalculatorToolComponent(LCToolComponent):\n display_name = \"Calculator\"\n description = \"Perform basic arithmetic operations on a given expression.\"\n icon = \"calculator\"\n name = \"CalculatorTool\"\n\n inputs = [\n MessageTextInput(\n name=\"expression\",\n display_name=\"Expression\",\n info=\"The arithmetic expression to evaluate (e.g., '4*4*(33/22)+12-20').\",\n ),\n ]\n\n class CalculatorToolSchema(BaseModel):\n expression: str = Field(..., description=\"The arithmetic expression to evaluate.\")\n\n def run_model(self) -> list[Data]:\n return self._evaluate_expression(self.expression)\n\n def build_tool(self) -> Tool:\n return StructuredTool.from_function(\n name=\"calculator\",\n description=\"Evaluate basic arithmetic expressions. Input should be a string containing the expression.\",\n func=self._eval_expr_with_error,\n args_schema=self.CalculatorToolSchema,\n )\n\n def _eval_expr(self, node):\n # Define the allowed operators\n operators = {\n ast.Add: operator.add,\n ast.Sub: operator.sub,\n ast.Mult: operator.mul,\n ast.Div: operator.truediv,\n ast.Pow: operator.pow,\n }\n if isinstance(node, ast.Num):\n return node.n\n if isinstance(node, ast.BinOp):\n return operators[type(node.op)](self._eval_expr(node.left), self._eval_expr(node.right))\n if isinstance(node, ast.UnaryOp):\n return operators[type(node.op)](self._eval_expr(node.operand))\n if isinstance(node, ast.Call):\n msg = (\n \"Function calls like sqrt(), sin(), cos() etc. are not supported. \"\n \"Only basic arithmetic operations (+, -, *, /, **) are allowed.\"\n )\n raise TypeError(msg)\n msg = f\"Unsupported operation or expression type: {type(node).__name__}\"\n raise TypeError(msg)\n\n def _eval_expr_with_error(self, expression: str) -> list[Data]:\n try:\n return self._evaluate_expression(expression)\n except Exception as e:\n raise ToolException(str(e)) from e\n\n def _evaluate_expression(self, expression: str) -> list[Data]:\n try:\n # Parse the expression and evaluate it\n tree = ast.parse(expression, mode=\"eval\")\n result = self._eval_expr(tree.body)\n\n # Format the result to a reasonable number of decimal places\n formatted_result = f\"{result:.6f}\".rstrip(\"0\").rstrip(\".\")\n\n self.status = formatted_result\n return [Data(data={\"result\": formatted_result})]\n\n except (SyntaxError, TypeError, KeyError) as e:\n error_message = f\"Invalid expression: {e}\"\n self.status = error_message\n return [Data(data={\"error\": error_message, \"input\": expression})]\n except ZeroDivisionError:\n error_message = \"Error: Division by zero\"\n self.status = error_message\n return [Data(data={\"error\": error_message, \"input\": expression})]\n except Exception as e: # noqa: BLE001\n logger.opt(exception=True).debug(\"Error evaluating expression\")\n error_message = f\"Error: {e}\"\n self.status = error_message\n return [Data(data={\"error\": error_message, \"input\": expression})]\n" + "value": "import ast\nimport operator\n\nfrom langchain.tools import StructuredTool\nfrom langchain_core.tools import ToolException\nfrom loguru import logger\nfrom pydantic import BaseModel, Field\n\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.field_typing import Tool\nfrom langflow.inputs import MessageTextInput\nfrom langflow.schema import Data\n\n\nclass CalculatorToolComponent(LCToolComponent):\n display_name = \"Calculator\"\n description = \"Perform basic arithmetic operations on a given expression.\"\n icon = \"calculator\"\n name = \"CalculatorTool\"\n\n inputs = [\n MessageTextInput(\n name=\"expression\",\n display_name=\"Expression\",\n info=\"The arithmetic expression to evaluate (e.g., '4*4*(33/22)+12-20').\",\n tool_mode=True,\n ),\n ]\n\n class CalculatorToolSchema(BaseModel):\n expression: str = Field(..., description=\"The arithmetic expression to evaluate.\")\n\n def run_model(self) -> list[Data]:\n return self._evaluate_expression(self.expression)\n\n def build_tool(self) -> Tool:\n return StructuredTool.from_function(\n name=\"calculator\",\n description=\"Evaluate basic arithmetic expressions. Input should be a string containing the expression.\",\n func=self._eval_expr_with_error,\n args_schema=self.CalculatorToolSchema,\n )\n\n def _eval_expr(self, node):\n # Define the allowed operators\n operators = {\n ast.Add: operator.add,\n ast.Sub: operator.sub,\n ast.Mult: operator.mul,\n ast.Div: operator.truediv,\n ast.Pow: operator.pow,\n }\n if isinstance(node, ast.Num):\n return node.n\n if isinstance(node, ast.BinOp):\n return operators[type(node.op)](self._eval_expr(node.left), self._eval_expr(node.right))\n if isinstance(node, ast.UnaryOp):\n return operators[type(node.op)](self._eval_expr(node.operand))\n if isinstance(node, ast.Call):\n msg = (\n \"Function calls like sqrt(), sin(), cos() etc. are not supported. \"\n \"Only basic arithmetic operations (+, -, *, /, **) are allowed.\"\n )\n raise TypeError(msg)\n msg = f\"Unsupported operation or expression type: {type(node).__name__}\"\n raise TypeError(msg)\n\n def _eval_expr_with_error(self, expression: str) -> list[Data]:\n try:\n return self._evaluate_expression(expression)\n except Exception as e:\n raise ToolException(str(e)) from e\n\n def _evaluate_expression(self, expression: str) -> list[Data]:\n try:\n # Parse the expression and evaluate it\n tree = ast.parse(expression, mode=\"eval\")\n result = self._eval_expr(tree.body)\n\n # Format the result to a reasonable number of decimal places\n formatted_result = f\"{result:.6f}\".rstrip(\"0\").rstrip(\".\")\n\n self.status = formatted_result\n return [Data(data={\"result\": formatted_result})]\n\n except (SyntaxError, TypeError, KeyError) as e:\n error_message = f\"Invalid expression: {e}\"\n self.status = error_message\n return [Data(data={\"error\": error_message, \"input\": expression})]\n except ZeroDivisionError:\n error_message = \"Error: Division by zero\"\n self.status = error_message\n return [Data(data={\"error\": error_message, \"input\": expression})]\n except Exception as e: # noqa: BLE001\n logger.opt(exception=True).debug(\"Error evaluating expression\")\n error_message = f\"Error: {e}\"\n self.status = error_message\n return [Data(data={\"error\": error_message, \"input\": expression})]\n" }, "expression": { "_input_type": "MessageTextInput", diff --git a/src/backend/base/langflow/inputs/inputs.py b/src/backend/base/langflow/inputs/inputs.py index 31335869a..a7346b3f2 100644 --- a/src/backend/base/langflow/inputs/inputs.py +++ b/src/backend/base/langflow/inputs/inputs.py @@ -67,7 +67,7 @@ class HandleInput(BaseInputMixin, ListableInputMixin, MetadataTraceMixin): field_type: SerializableFieldTypes = FieldTypes.OTHER -class DataInput(HandleInput, InputTraceMixin, ListableInputMixin): +class DataInput(HandleInput, InputTraceMixin, ListableInputMixin, ToolModeMixin): """Represents an Input that has a Handle that receives a Data object. Attributes: @@ -77,7 +77,7 @@ class DataInput(HandleInput, InputTraceMixin, ListableInputMixin): input_types: list[str] = ["Data"] -class PromptInput(BaseInputMixin, ListableInputMixin, InputTraceMixin): +class PromptInput(BaseInputMixin, ListableInputMixin, InputTraceMixin, ToolModeMixin): field_type: SerializableFieldTypes = FieldTypes.PROMPT @@ -209,7 +209,7 @@ class MessageTextInput(StrInput, MetadataTraceMixin, InputTraceMixin, ToolModeMi return value -class MultilineInput(MessageTextInput, MultilineMixin, InputTraceMixin): +class MultilineInput(MessageTextInput, MultilineMixin, InputTraceMixin, ToolModeMixin): """Represents a multiline input field. Attributes: @@ -407,7 +407,7 @@ class DictInput(BaseInputMixin, ListableInputMixin, InputTraceMixin): value: dict | None = {} -class DropdownInput(BaseInputMixin, DropDownMixin, MetadataTraceMixin): +class DropdownInput(BaseInputMixin, DropDownMixin, MetadataTraceMixin, ToolModeMixin): """Represents a dropdown input field. This class represents a dropdown input field and provides functionality for handling dropdown values. diff --git a/src/frontend/tests/core/features/stop-building.spec.ts b/src/frontend/tests/core/features/stop-building.spec.ts index c9260db69..d4c37578e 100644 --- a/src/frontend/tests/core/features/stop-building.spec.ts +++ b/src/frontend/tests/core/features/stop-building.spec.ts @@ -231,7 +231,7 @@ class CustomComponent(Component): name = "CustomComponent" inputs = [ - MessageTextInput(name="input_value", display_name="Input Value", value="Hello, World!"), + MessageTextInput(name="input_value", display_name="Input Value", value="Hello, World!", tool_mode=True), ] outputs = [ diff --git a/src/frontend/tests/core/unit/linkComponent.spec.ts b/src/frontend/tests/core/unit/linkComponent.spec.ts index 251614858..96d1d9996 100644 --- a/src/frontend/tests/core/unit/linkComponent.spec.ts +++ b/src/frontend/tests/core/unit/linkComponent.spec.ts @@ -55,18 +55,31 @@ test("user should interact with link component", async ({ context, page }) => { let cleanCode = await extractAndCleanCode(page); - // Replace the import statement - cleanCode = cleanCode.replace( - "from langflow.io import MessageTextInput, Output", - "from langflow.io import MessageTextInput, Output, LinkInput", - ); - - // Replace the MessageTextInput line and add LinkInput - cleanCode = cleanCode.replace( - 'MessageTextInput(name="input_value", display_name="Input Value", value="Hello, World!", tool_mode=True),', - `MessageTextInput(name="input_value", display_name="Input Value", value="Hello, World!", tool_mode=True), - LinkInput(name="link", display_name="BUTTON", value="https://www.datastax.com", text="Click me"),`, - ); + // Use regex pattern to match the imports section more flexibly + cleanCode = updateComponentCode(cleanCode, { + imports: ["MessageTextInput", "Output", "LinkInput"], + inputs: [ + { + name: "MessageTextInput", + config: { + name: "input_value", + display_name: "Input Value", + info: "This is a custom component Input", + value: "Hello, World!", + tool_mode: true, + }, + }, + { + name: "LinkInput", + config: { + name: "link", + display_name: "BUTTON", + value: "https://www.datastax.com", + text: "Click me", + }, + }, + ], + }); await page.locator("textarea").last().press(`${control}+a`); await page.keyboard.press("Backspace"); @@ -103,3 +116,44 @@ async function extractAndCleanCode(page: Page): Promise { return codeContent; } + +function updateComponentCode( + code: string, + updates: { + imports?: string[]; + inputs?: Array<{ name: string; config: Record }>; + }, +): string { + let updatedCode = code; + + // Update imports + if (updates.imports) { + const importPattern = /from\s+langflow\.io\s+import\s+([^;\n]+)/; + const newImports = updates.imports.join(", "); + updatedCode = updatedCode.replace( + importPattern, + `from langflow.io import ${newImports}`, + ); + } + + // Update inputs + if (updates.inputs) { + const inputsPattern = /inputs\s*=\s*\[([\s\S]*?)\]/; + const newInputs = updates.inputs + .map(({ name, config }) => { + const params = Object.entries(config) + .map(([key, value]) => `${key}=${JSON.stringify(value)}`) + .join(",\n "); + return ` ${name}(\n ${params}\n )`; + }) + .join(",\n"); + updatedCode = updatedCode.replace( + inputsPattern, + `inputs = [\n${newInputs}\n ]`, + ); + updatedCode = updatedCode.replace("true", "True"); + updatedCode = updatedCode.replace("false", "False"); + } + + return updatedCode; +}