refactor: update dependency versions and streamline backend installation commands

Makefile

@@ -64,13 +64,11 @@ help: ## show this help message
 
 reinstall_backend: ## forces reinstall all dependencies (no caching)
 	@echo 'Installing backend dependencies'
-	#@poetry install > /dev/null 2>&1
-	@cd src/backend/base && uv sync -n --reinstall && cd ../../../ && uv sync -n --reinstall > /dev/null 2>&1
+	@uv sync -n --reinstall --frozen
 
 install_backend: ## install the backend dependencies
 	@echo 'Installing backend dependencies'
-	#@poetry install > /dev/null 2>&1
-	@cd src/backend/base && uv sync && cd ../../../ && uv sync > /dev/null 2>&1
+	@uv sync --frozen
 
 install_frontend: ## install the frontend dependencies
 	@echo 'Installing frontend dependencies'
@@ -141,7 +139,7 @@ coverage: ## run the tests and generate a coverage report
 	#@poetry run coverage erase
 
 unit_tests: ## run unit tests
-	cd src/backend/base && uv sync --extra dev && cd ../../../ && uv sync --extra dev > /dev/null 2>&1
+	@uv sync --extra dev --frozen
 ifeq ($(async), true)
 	uv run pytest src/backend/tests \
 		--ignore=src/backend/tests/integration \

pyproject.toml

@@ -48,7 +48,7 @@ dependencies = [
     "pyarrow>=14.0.0",
     "wikipedia>=1.4.0",
     "qdrant-client>=1.9.0",
-    "weaviate-client",
+    "weaviate-client>=4.8",
     "cohere>=5.5.3",
     "faiss-cpu>=1.8.0",
     "types-cachetools>=5.3.0.5",
@@ -131,7 +131,7 @@ local = [
     "ctransformers>=0.2.10"
 ]
 clickhouse-connect = [
-    "clickhouse-connect[clickhouse-connect]==0.7.19"
+    "clickhouse-connect==0.7.19"
 ]
 
 [project.scripts]
@@ -275,7 +275,7 @@ dev-dependencies = [
     "ipykernel>=6.29.0",
     "mypy>=1.11.0",
     "ruff>=0.6.2,<0.7.0",
-    "httpx",
+    "httpx>=0.27.0",
     "pytest>=8.2.0",
     "types-requests>=2.32.0",
     "requests>=2.32.0",

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

@@ -1211,7 +1211,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import ast\nimport operator\nfrom typing import List\n\nfrom langchain.tools import StructuredTool\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._evaluate_expression,\n            args_schema=self.CalculatorToolSchema,\n        )\n\n    def _evaluate_expression(self, expression: str) -> List[Data]:\n        try:\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\n            def eval_expr(node):\n                if isinstance(node, ast.Num):\n                    return node.n\n                elif isinstance(node, ast.BinOp):\n                    return operators[type(node.op)](eval_expr(node.left), eval_expr(node.right))\n                elif isinstance(node, ast.UnaryOp):\n                    return operators[type(node.op)](eval_expr(node.operand))\n                else:\n                    raise TypeError(node)\n\n            # Parse the expression and evaluate it\n            tree = ast.parse(expression, mode=\"eval\")\n            result = 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: {str(e)}\"\n            self.status = error_message\n            return [Data(data={\"error\": error_message})]\n        except ZeroDivisionError:\n            error_message = \"Error: Division by zero\"\n            self.status = error_message\n            return [Data(data={\"error\": error_message})]\n        except Exception as e:\n            error_message = f\"Error: {str(e)}\"\n            self.status = error_message\n            return [Data(data={\"error\": error_message})]\n"
+                "value": "import ast\nimport operator\n\nfrom langchain.tools import StructuredTool\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._evaluate_expression,\n            args_schema=self.CalculatorToolSchema,\n        )\n\n    def _evaluate_expression(self, expression: str) -> list[Data]:\n        try:\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\n            def eval_expr(node):\n                if isinstance(node, ast.Num):\n                    return node.n\n                elif isinstance(node, ast.BinOp):\n                    return operators[type(node.op)](eval_expr(node.left), eval_expr(node.right))\n                elif isinstance(node, ast.UnaryOp):\n                    return operators[type(node.op)](eval_expr(node.operand))\n                else:\n                    raise TypeError(node)\n\n            # Parse the expression and evaluate it\n            tree = ast.parse(expression, mode=\"eval\")\n            result = 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: {str(e)}\"\n            self.status = error_message\n            return [Data(data={\"error\": error_message})]\n        except ZeroDivisionError:\n            error_message = \"Error: Division by zero\"\n            self.status = error_message\n            return [Data(data={\"error\": error_message})]\n        except Exception as e:\n            error_message = f\"Error: {str(e)}\"\n            self.status = error_message\n            return [Data(data={\"error\": error_message})]\n"
               },
               "expression": {
                 "_input_type": "MessageTextInput",
@@ -1321,7 +1321,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import importlib\nfrom typing import List, Union\n\nfrom langchain.tools import StructuredTool\nfrom langchain_experimental.utilities import PythonREPL\nfrom pydantic import BaseModel, Field\n\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.field_typing import Tool\nfrom langflow.inputs import StrInput\nfrom langflow.schema import Data\n\n\nclass PythonREPLToolComponent(LCToolComponent):\n    display_name = \"Python REPL Tool\"\n    description = \"A tool for running Python code in a REPL environment.\"\n    name = \"PythonREPLTool\"\n\n    inputs = [\n        StrInput(\n            name=\"name\",\n            display_name=\"Tool Name\",\n            info=\"The name of the tool.\",\n            value=\"python_repl\",\n        ),\n        StrInput(\n            name=\"description\",\n            display_name=\"Tool Description\",\n            info=\"A description of the tool.\",\n            value=\"A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`.\",\n        ),\n        StrInput(\n            name=\"global_imports\",\n            display_name=\"Global Imports\",\n            info=\"A comma-separated list of modules to import globally, e.g. 'math,numpy'.\",\n            value=\"math\",\n        ),\n        StrInput(\n            name=\"code\",\n            display_name=\"Python Code\",\n            info=\"The Python code to execute.\",\n            value=\"print('Hello, World!')\",\n        ),\n    ]\n\n    class PythonREPLSchema(BaseModel):\n        code: str = Field(..., description=\"The Python code to execute.\")\n\n    def get_globals(self, global_imports: Union[str, List[str]]) -> dict:\n        global_dict = {}\n        if isinstance(global_imports, str):\n            modules = [module.strip() for module in global_imports.split(\",\")]\n        elif isinstance(global_imports, list):\n            modules = global_imports\n        else:\n            raise ValueError(\"global_imports must be either a string or a list\")\n\n        for module in modules:\n            try:\n                imported_module = importlib.import_module(module)\n                global_dict[imported_module.__name__] = imported_module\n            except ImportError:\n                raise ImportError(f\"Could not import module {module}\")\n        return global_dict\n\n    def build_tool(self) -> Tool:\n        _globals = self.get_globals(self.global_imports)\n        python_repl = PythonREPL(_globals=_globals)\n\n        def run_python_code(code: str) -> str:\n            try:\n                return python_repl.run(code)\n            except Exception as e:\n                return f\"Error: {str(e)}\"\n\n        tool = StructuredTool.from_function(\n            name=self.name,\n            description=self.description,\n            func=run_python_code,\n            args_schema=self.PythonREPLSchema,\n        )\n\n        self.status = f\"Python REPL Tool created with global imports: {self.global_imports}\"\n        return tool\n\n    def run_model(self) -> List[Data]:\n        tool = self.build_tool()\n        result = tool.run(self.code)\n        return [Data(data={\"result\": result})]\n"
+                "value": "import importlib\n\nfrom langchain.tools import StructuredTool\nfrom langchain_experimental.utilities import PythonREPL\nfrom pydantic import BaseModel, Field\n\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.field_typing import Tool\nfrom langflow.inputs import StrInput\nfrom langflow.schema import Data\n\n\nclass PythonREPLToolComponent(LCToolComponent):\n    display_name = \"Python REPL Tool\"\n    description = \"A tool for running Python code in a REPL environment.\"\n    name = \"PythonREPLTool\"\n\n    inputs = [\n        StrInput(\n            name=\"name\",\n            display_name=\"Tool Name\",\n            info=\"The name of the tool.\",\n            value=\"python_repl\",\n        ),\n        StrInput(\n            name=\"description\",\n            display_name=\"Tool Description\",\n            info=\"A description of the tool.\",\n            value=\"A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`.\",\n        ),\n        StrInput(\n            name=\"global_imports\",\n            display_name=\"Global Imports\",\n            info=\"A comma-separated list of modules to import globally, e.g. 'math,numpy'.\",\n            value=\"math\",\n        ),\n        StrInput(\n            name=\"code\",\n            display_name=\"Python Code\",\n            info=\"The Python code to execute.\",\n            value=\"print('Hello, World!')\",\n        ),\n    ]\n\n    class PythonREPLSchema(BaseModel):\n        code: str = Field(..., description=\"The Python code to execute.\")\n\n    def get_globals(self, global_imports: str | list[str]) -> dict:\n        global_dict = {}\n        if isinstance(global_imports, str):\n            modules = [module.strip() for module in global_imports.split(\",\")]\n        elif isinstance(global_imports, list):\n            modules = global_imports\n        else:\n            raise ValueError(\"global_imports must be either a string or a list\")\n\n        for module in modules:\n            try:\n                imported_module = importlib.import_module(module)\n                global_dict[imported_module.__name__] = imported_module\n            except ImportError:\n                raise ImportError(f\"Could not import module {module}\")\n        return global_dict\n\n    def build_tool(self) -> Tool:\n        _globals = self.get_globals(self.global_imports)\n        python_repl = PythonREPL(_globals=_globals)\n\n        def run_python_code(code: str) -> str:\n            try:\n                return python_repl.run(code)\n            except Exception as e:\n                return f\"Error: {str(e)}\"\n\n        tool = StructuredTool.from_function(\n            name=self.name,\n            description=self.description,\n            func=run_python_code,\n            args_schema=self.PythonREPLSchema,\n        )\n\n        self.status = f\"Python REPL Tool created with global imports: {self.global_imports}\"\n        return tool\n\n    def run_model(self) -> list[Data]:\n        tool = self.build_tool()\n        result = tool.run(self.code)\n        return [Data(data={\"result\": result})]\n"
               },
               "description": {
                 "_input_type": "StrInput",