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refactor: update dependency versions and streamline backend installation commands (#3939)

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* Update Makefile to streamline backend dependency installation commands

* Update dependency versions in pyproject.toml for weaviate-client, httpx, and others

* Update dependency versions in pyproject.toml for better compatibility and stability

* new lock

* refactor: streamline backend dependency installation commands

* update examples formatting
This commit is contained in:
Gabriel Luiz Freitas Almeida 2024-09-26 16:23:26 -03:00 • committed by GitHub
commit bf2aadf6a4
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9 changed files with 73 additions and 60 deletions
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8
Makefile
View file

@ -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 \

6
pyproject.toml
View file

@ -49,7 +49,7 @@ 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",
@ -132,7 +132,7 @@ local = [
"ctransformers>=0.2.10"
]
clickhouse-connect = [
"clickhouse-connect[clickhouse-connect]==0.7.19"
"clickhouse-connect==0.7.19"
]
[project.scripts]
@ -276,7 +276,7 @@ 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",

4
src/backend/base/langflow/initial_setup/starter_projects/Agent Flow.json
View file

@ -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",

2
src/backend/base/langflow/initial_setup/starter_projects/Complex Agent.json
View file

@ -4109,7 +4109,7 @@
"show": true,
"title_case": false,
"type": "code",
"value": "from typing import Any, Dict, List, Optional\n\nfrom langchain.tools import StructuredTool\nfrom langchain_community.utilities.searchapi import SearchApiAPIWrapper\nfrom pydantic import BaseModel, Field\n\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.field_typing import Tool\nfrom langflow.inputs import DictInput, IntInput, MessageTextInput, MultilineInput, SecretStrInput\nfrom langflow.schema import Data\n\n\nclass SearchAPIComponent(LCToolComponent):\n display_name: str = \"Search API\"\n description: str = \"Call the searchapi.io API with result limiting\"\n name = \"SearchAPI\"\n documentation: str = \"https://www.searchapi.io/docs/google\"\n\n inputs = [\n MessageTextInput(name=\"engine\", display_name=\"Engine\", value=\"google\"),\n SecretStrInput(name=\"api_key\", display_name=\"SearchAPI API Key\", required=True),\n MultilineInput(\n name=\"input_value\",\n display_name=\"Input\",\n ),\n DictInput(name=\"search_params\", display_name=\"Search parameters\", advanced=True, is_list=True),\n IntInput(name=\"max_results\", display_name=\"Max Results\", value=5, advanced=True),\n IntInput(name=\"max_snippet_length\", display_name=\"Max Snippet Length\", value=100, advanced=True),\n ]\n\n class SearchAPISchema(BaseModel):\n query: str = Field(..., description=\"The search query\")\n params: Optional[Dict[str, Any]] = Field(default_factory=dict, description=\"Additional search parameters\")\n max_results: int = Field(5, description=\"Maximum number of results to return\")\n max_snippet_length: int = Field(100, description=\"Maximum length of each result snippet\")\n\n def _build_wrapper(self):\n return SearchApiAPIWrapper(engine=self.engine, searchapi_api_key=self.api_key)\n\n def build_tool(self) -> Tool:\n wrapper = self._build_wrapper()\n\n def search_func(\n query: str, params: Optional[Dict[str, Any]] = None, max_results: int = 5, max_snippet_length: int = 100\n ) -> List[Dict[str, Any]]:\n params = params or {}\n full_results = wrapper.results(query=query, **params)\n organic_results = full_results.get(\"organic_results\", [])[:max_results]\n\n limited_results = []\n for result in organic_results:\n limited_result = {\n \"title\": result.get(\"title\", \"\")[:max_snippet_length],\n \"link\": result.get(\"link\", \"\"),\n \"snippet\": result.get(\"snippet\", \"\")[:max_snippet_length],\n }\n limited_results.append(limited_result)\n\n return limited_results\n\n tool = StructuredTool.from_function(\n name=\"search_api\",\n description=\"Search for recent results using searchapi.io with result limiting\",\n func=search_func,\n args_schema=self.SearchAPISchema,\n )\n\n self.status = f\"Search API Tool created with engine: {self.engine}\"\n return tool\n\n def run_model(self) -> List[Data]:\n tool = self.build_tool()\n results = tool.run(\n {\n \"query\": self.input_value,\n \"params\": self.search_params or {},\n \"max_results\": self.max_results,\n \"max_snippet_length\": self.max_snippet_length,\n }\n )\n\n data_list = [Data(data=result, text=result.get(\"snippet\", \"\")) for result in results]\n\n self.status = data_list\n return data_list\n"
"value": "from typing import Any\n\nfrom langchain.tools import StructuredTool\nfrom langchain_community.utilities.searchapi import SearchApiAPIWrapper\nfrom pydantic import BaseModel, Field\n\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.field_typing import Tool\nfrom langflow.inputs import DictInput, IntInput, MessageTextInput, MultilineInput, SecretStrInput\nfrom langflow.schema import Data\n\n\nclass SearchAPIComponent(LCToolComponent):\n display_name: str = \"Search API\"\n description: str = \"Call the searchapi.io API with result limiting\"\n name = \"SearchAPI\"\n documentation: str = \"https://www.searchapi.io/docs/google\"\n\n inputs = [\n MessageTextInput(name=\"engine\", display_name=\"Engine\", value=\"google\"),\n SecretStrInput(name=\"api_key\", display_name=\"SearchAPI API Key\", required=True),\n MultilineInput(\n name=\"input_value\",\n display_name=\"Input\",\n ),\n DictInput(name=\"search_params\", display_name=\"Search parameters\", advanced=True, is_list=True),\n IntInput(name=\"max_results\", display_name=\"Max Results\", value=5, advanced=True),\n IntInput(name=\"max_snippet_length\", display_name=\"Max Snippet Length\", value=100, advanced=True),\n ]\n\n class SearchAPISchema(BaseModel):\n query: str = Field(..., description=\"The search query\")\n params: dict[str, Any] | None = Field(default_factory=dict, description=\"Additional search parameters\")\n max_results: int = Field(5, description=\"Maximum number of results to return\")\n max_snippet_length: int = Field(100, description=\"Maximum length of each result snippet\")\n\n def _build_wrapper(self):\n return SearchApiAPIWrapper(engine=self.engine, searchapi_api_key=self.api_key)\n\n def build_tool(self) -> Tool:\n wrapper = self._build_wrapper()\n\n def search_func(\n query: str, params: dict[str, Any] | None = None, max_results: int = 5, max_snippet_length: int = 100\n ) -> list[dict[str, Any]]:\n params = params or {}\n full_results = wrapper.results(query=query, **params)\n organic_results = full_results.get(\"organic_results\", [])[:max_results]\n\n limited_results = []\n for result in organic_results:\n limited_result = {\n \"title\": result.get(\"title\", \"\")[:max_snippet_length],\n \"link\": result.get(\"link\", \"\"),\n \"snippet\": result.get(\"snippet\", \"\")[:max_snippet_length],\n }\n limited_results.append(limited_result)\n\n return limited_results\n\n tool = StructuredTool.from_function(\n name=\"search_api\",\n description=\"Search for recent results using searchapi.io with result limiting\",\n func=search_func,\n args_schema=self.SearchAPISchema,\n )\n\n self.status = f\"Search API Tool created with engine: {self.engine}\"\n return tool\n\n def run_model(self) -> list[Data]:\n tool = self.build_tool()\n results = tool.run(\n {\n \"query\": self.input_value,\n \"params\": self.search_params or {},\n \"max_results\": self.max_results,\n \"max_snippet_length\": self.max_snippet_length,\n }\n )\n\n data_list = [Data(data=result, text=result.get(\"snippet\", \"\")) for result in results]\n\n self.status = data_list\n return data_list\n"
},
"engine": {
"advanced": false,

2
src/backend/base/langflow/initial_setup/starter_projects/Hierarchical Agent.json
View file

@ -2668,7 +2668,7 @@
"show": true,
"title_case": false,
"type": "code",
"value": "from typing import Any, Dict, List, Optional\n\nfrom langchain.tools import StructuredTool\nfrom langchain_community.utilities.searchapi import SearchApiAPIWrapper\nfrom pydantic import BaseModel, Field\n\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.field_typing import Tool\nfrom langflow.inputs import DictInput, IntInput, MessageTextInput, MultilineInput, SecretStrInput\nfrom langflow.schema import Data\n\n\nclass SearchAPIComponent(LCToolComponent):\n display_name: str = \"Search API\"\n description: str = \"Call the searchapi.io API with result limiting\"\n name = \"SearchAPI\"\n documentation: str = \"https://www.searchapi.io/docs/google\"\n\n inputs = [\n MessageTextInput(name=\"engine\", display_name=\"Engine\", value=\"google\"),\n SecretStrInput(name=\"api_key\", display_name=\"SearchAPI API Key\", required=True),\n MultilineInput(\n name=\"input_value\",\n display_name=\"Input\",\n ),\n DictInput(name=\"search_params\", display_name=\"Search parameters\", advanced=True, is_list=True),\n IntInput(name=\"max_results\", display_name=\"Max Results\", value=5, advanced=True),\n IntInput(name=\"max_snippet_length\", display_name=\"Max Snippet Length\", value=100, advanced=True),\n ]\n\n class SearchAPISchema(BaseModel):\n query: str = Field(..., description=\"The search query\")\n params: Optional[Dict[str, Any]] = Field(default_factory=dict, description=\"Additional search parameters\")\n max_results: int = Field(5, description=\"Maximum number of results to return\")\n max_snippet_length: int = Field(100, description=\"Maximum length of each result snippet\")\n\n def _build_wrapper(self):\n return SearchApiAPIWrapper(engine=self.engine, searchapi_api_key=self.api_key)\n\n def build_tool(self) -> Tool:\n wrapper = self._build_wrapper()\n\n def search_func(\n query: str, params: Optional[Dict[str, Any]] = None, max_results: int = 5, max_snippet_length: int = 100\n ) -> List[Dict[str, Any]]:\n params = params or {}\n full_results = wrapper.results(query=query, **params)\n organic_results = full_results.get(\"organic_results\", [])[:max_results]\n\n limited_results = []\n for result in organic_results:\n limited_result = {\n \"title\": result.get(\"title\", \"\")[:max_snippet_length],\n \"link\": result.get(\"link\", \"\"),\n \"snippet\": result.get(\"snippet\", \"\")[:max_snippet_length],\n }\n limited_results.append(limited_result)\n\n return limited_results\n\n tool = StructuredTool.from_function(\n name=\"search_api\",\n description=\"Search for recent results using searchapi.io with result limiting\",\n func=search_func,\n args_schema=self.SearchAPISchema,\n )\n\n self.status = f\"Search API Tool created with engine: {self.engine}\"\n return tool\n\n def run_model(self) -> List[Data]:\n tool = self.build_tool()\n results = tool.run(\n {\n \"query\": self.input_value,\n \"params\": self.search_params or {},\n \"max_results\": self.max_results,\n \"max_snippet_length\": self.max_snippet_length,\n }\n )\n\n data_list = [Data(data=result, text=result.get(\"snippet\", \"\")) for result in results]\n\n self.status = data_list\n return data_list\n"
"value": "from typing import Any\n\nfrom langchain.tools import StructuredTool\nfrom langchain_community.utilities.searchapi import SearchApiAPIWrapper\nfrom pydantic import BaseModel, Field\n\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.field_typing import Tool\nfrom langflow.inputs import DictInput, IntInput, MessageTextInput, MultilineInput, SecretStrInput\nfrom langflow.schema import Data\n\n\nclass SearchAPIComponent(LCToolComponent):\n display_name: str = \"Search API\"\n description: str = \"Call the searchapi.io API with result limiting\"\n name = \"SearchAPI\"\n documentation: str = \"https://www.searchapi.io/docs/google\"\n\n inputs = [\n MessageTextInput(name=\"engine\", display_name=\"Engine\", value=\"google\"),\n SecretStrInput(name=\"api_key\", display_name=\"SearchAPI API Key\", required=True),\n MultilineInput(\n name=\"input_value\",\n display_name=\"Input\",\n ),\n DictInput(name=\"search_params\", display_name=\"Search parameters\", advanced=True, is_list=True),\n IntInput(name=\"max_results\", display_name=\"Max Results\", value=5, advanced=True),\n IntInput(name=\"max_snippet_length\", display_name=\"Max Snippet Length\", value=100, advanced=True),\n ]\n\n class SearchAPISchema(BaseModel):\n query: str = Field(..., description=\"The search query\")\n params: dict[str, Any] | None = Field(default_factory=dict, description=\"Additional search parameters\")\n max_results: int = Field(5, description=\"Maximum number of results to return\")\n max_snippet_length: int = Field(100, description=\"Maximum length of each result snippet\")\n\n def _build_wrapper(self):\n return SearchApiAPIWrapper(engine=self.engine, searchapi_api_key=self.api_key)\n\n def build_tool(self) -> Tool:\n wrapper = self._build_wrapper()\n\n def search_func(\n query: str, params: dict[str, Any] | None = None, max_results: int = 5, max_snippet_length: int = 100\n ) -> list[dict[str, Any]]:\n params = params or {}\n full_results = wrapper.results(query=query, **params)\n organic_results = full_results.get(\"organic_results\", [])[:max_results]\n\n limited_results = []\n for result in organic_results:\n limited_result = {\n \"title\": result.get(\"title\", \"\")[:max_snippet_length],\n \"link\": result.get(\"link\", \"\"),\n \"snippet\": result.get(\"snippet\", \"\")[:max_snippet_length],\n }\n limited_results.append(limited_result)\n\n return limited_results\n\n tool = StructuredTool.from_function(\n name=\"search_api\",\n description=\"Search for recent results using searchapi.io with result limiting\",\n func=search_func,\n args_schema=self.SearchAPISchema,\n )\n\n self.status = f\"Search API Tool created with engine: {self.engine}\"\n return tool\n\n def run_model(self) -> list[Data]:\n tool = self.build_tool()\n results = tool.run(\n {\n \"query\": self.input_value,\n \"params\": self.search_params or {},\n \"max_results\": self.max_results,\n \"max_snippet_length\": self.max_snippet_length,\n }\n )\n\n data_list = [Data(data=result, text=result.get(\"snippet\", \"\")) for result in results]\n\n self.status = data_list\n return data_list\n"
},
"engine": {
"advanced": false,

4
src/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.json
View file

@ -1469,7 +1469,7 @@
"show": true,
"title_case": false,
"type": "code",
"value": "from typing import Any, Dict, List, Optional\n\nfrom langchain.tools import StructuredTool\nfrom langchain_community.utilities.searchapi import SearchApiAPIWrapper\nfrom pydantic import BaseModel, Field\n\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.field_typing import Tool\nfrom langflow.inputs import DictInput, IntInput, MessageTextInput, MultilineInput, SecretStrInput\nfrom langflow.schema import Data\n\n\nclass SearchAPIComponent(LCToolComponent):\n display_name: str = \"Search API\"\n description: str = \"Call the searchapi.io API with result limiting\"\n name = \"SearchAPI\"\n documentation: str = \"https://www.searchapi.io/docs/google\"\n\n inputs = [\n MessageTextInput(name=\"engine\", display_name=\"Engine\", value=\"google\"),\n SecretStrInput(name=\"api_key\", display_name=\"SearchAPI API Key\", required=True),\n MultilineInput(\n name=\"input_value\",\n display_name=\"Input\",\n ),\n DictInput(name=\"search_params\", display_name=\"Search parameters\", advanced=True, is_list=True),\n IntInput(name=\"max_results\", display_name=\"Max Results\", value=5, advanced=True),\n IntInput(name=\"max_snippet_length\", display_name=\"Max Snippet Length\", value=100, advanced=True),\n ]\n\n class SearchAPISchema(BaseModel):\n query: str = Field(..., description=\"The search query\")\n params: Optional[Dict[str, Any]] = Field(default_factory=dict, description=\"Additional search parameters\")\n max_results: int = Field(5, description=\"Maximum number of results to return\")\n max_snippet_length: int = Field(100, description=\"Maximum length of each result snippet\")\n\n def _build_wrapper(self):\n return SearchApiAPIWrapper(engine=self.engine, searchapi_api_key=self.api_key)\n\n def build_tool(self) -> Tool:\n wrapper = self._build_wrapper()\n\n def search_func(\n query: str, params: Optional[Dict[str, Any]] = None, max_results: int = 5, max_snippet_length: int = 100\n ) -> List[Dict[str, Any]]:\n params = params or {}\n full_results = wrapper.results(query=query, **params)\n organic_results = full_results.get(\"organic_results\", [])[:max_results]\n\n limited_results = []\n for result in organic_results:\n limited_result = {\n \"title\": result.get(\"title\", \"\")[:max_snippet_length],\n \"link\": result.get(\"link\", \"\"),\n \"snippet\": result.get(\"snippet\", \"\")[:max_snippet_length],\n }\n limited_results.append(limited_result)\n\n return limited_results\n\n tool = StructuredTool.from_function(\n name=\"search_api\",\n description=\"Search for recent results using searchapi.io with result limiting\",\n func=search_func,\n args_schema=self.SearchAPISchema,\n )\n\n self.status = f\"Search API Tool created with engine: {self.engine}\"\n return tool\n\n def run_model(self) -> List[Data]:\n tool = self.build_tool()\n results = tool.run(\n {\n \"query\": self.input_value,\n \"params\": self.search_params or {},\n \"max_results\": self.max_results,\n \"max_snippet_length\": self.max_snippet_length,\n }\n )\n\n data_list = [Data(data=result, text=result.get(\"snippet\", \"\")) for result in results]\n\n self.status = data_list\n return data_list\n"
"value": "from typing import Any\n\nfrom langchain.tools import StructuredTool\nfrom langchain_community.utilities.searchapi import SearchApiAPIWrapper\nfrom pydantic import BaseModel, Field\n\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.field_typing import Tool\nfrom langflow.inputs import DictInput, IntInput, MessageTextInput, MultilineInput, SecretStrInput\nfrom langflow.schema import Data\n\n\nclass SearchAPIComponent(LCToolComponent):\n display_name: str = \"Search API\"\n description: str = \"Call the searchapi.io API with result limiting\"\n name = \"SearchAPI\"\n documentation: str = \"https://www.searchapi.io/docs/google\"\n\n inputs = [\n MessageTextInput(name=\"engine\", display_name=\"Engine\", value=\"google\"),\n SecretStrInput(name=\"api_key\", display_name=\"SearchAPI API Key\", required=True),\n MultilineInput(\n name=\"input_value\",\n display_name=\"Input\",\n ),\n DictInput(name=\"search_params\", display_name=\"Search parameters\", advanced=True, is_list=True),\n IntInput(name=\"max_results\", display_name=\"Max Results\", value=5, advanced=True),\n IntInput(name=\"max_snippet_length\", display_name=\"Max Snippet Length\", value=100, advanced=True),\n ]\n\n class SearchAPISchema(BaseModel):\n query: str = Field(..., description=\"The search query\")\n params: dict[str, Any] | None = Field(default_factory=dict, description=\"Additional search parameters\")\n max_results: int = Field(5, description=\"Maximum number of results to return\")\n max_snippet_length: int = Field(100, description=\"Maximum length of each result snippet\")\n\n def _build_wrapper(self):\n return SearchApiAPIWrapper(engine=self.engine, searchapi_api_key=self.api_key)\n\n def build_tool(self) -> Tool:\n wrapper = self._build_wrapper()\n\n def search_func(\n query: str, params: dict[str, Any] | None = None, max_results: int = 5, max_snippet_length: int = 100\n ) -> list[dict[str, Any]]:\n params = params or {}\n full_results = wrapper.results(query=query, **params)\n organic_results = full_results.get(\"organic_results\", [])[:max_results]\n\n limited_results = []\n for result in organic_results:\n limited_result = {\n \"title\": result.get(\"title\", \"\")[:max_snippet_length],\n \"link\": result.get(\"link\", \"\"),\n \"snippet\": result.get(\"snippet\", \"\")[:max_snippet_length],\n }\n limited_results.append(limited_result)\n\n return limited_results\n\n tool = StructuredTool.from_function(\n name=\"search_api\",\n description=\"Search for recent results using searchapi.io with result limiting\",\n func=search_func,\n args_schema=self.SearchAPISchema,\n )\n\n self.status = f\"Search API Tool created with engine: {self.engine}\"\n return tool\n\n def run_model(self) -> list[Data]:\n tool = self.build_tool()\n results = tool.run(\n {\n \"query\": self.input_value,\n \"params\": self.search_params or {},\n \"max_results\": self.max_results,\n \"max_snippet_length\": self.max_snippet_length,\n }\n )\n\n data_list = [Data(data=result, text=result.get(\"snippet\", \"\")) for result in results]\n\n self.status = data_list\n return data_list\n"
},
"engine": {
"_input_type": "MessageTextInput",
@ -2310,7 +2310,7 @@
"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",

6
src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json
View file

File diff suppressed because one or more lines are too long

29
src/backend/base/pyproject.toml
View file

@ -166,7 +166,7 @@ readme = "README.md"
dependencies = [
"fastapi>=0.111.0",
"httpx",
"httpx>=0.27",
"uvicorn>=0.30.0",
"gunicorn>=22.0.0",
"langchain~=0.2.0",
@ -178,7 +178,6 @@ dependencies = [
"langchain-experimental>=0.0.61",
"pydantic>=2.7.0",
"pydantic-settings>=2.2.0",
"websockets",
"typer>=0.12.0",
"cachetools>=5.3.1",
"platformdirs>=4.2.0",
@ -224,22 +223,22 @@ dependencies = [
# Optional dependencies for uv
[project.optional-dependencies]
deploy = [
"celery",
"redis",
"flower"
"celery>=5.3.1",
"redis>=4.6.0",
"flower>=1.0.0"
]
local = [
"llama-cpp-python",
"sentence-transformers",
"ctransformers"
"llama-cpp-python>=0.2.0",
"sentence-transformers>=2.0.0",
"ctransformers>=0.2"
]
all = [
"celery",
"redis",
"flower",
"llama-cpp-python",
"sentence-transformers",
"ctransformers"
"celery>=5.3.1",
"redis>=4.6.0",
"flower>=1.0.0",
"llama-cpp-python>=0.2.0",
"sentence-transformers>=2.0.0",
"ctransformers>=0.2"
]
# Development dependencies
@ -248,7 +247,7 @@ dev = [
"ipykernel>=6.29.0",
"mypy>=1.11.0",
"ruff>=0.4.5",
"httpx",
"httpx>=0.27",
"pytest>=8.2.0",
"types-requests>=2.32.0",
"requests>=2.32.0",

72
uv.lock generated
View file

@ -335,14 +335,14 @@ wheels = [
[[package]]
name = "authlib"
version = "1.3.2"
version = "1.3.1"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "cryptography" },
]
sdist = { url = "https://files.pythonhosted.org/packages/f3/75/47dbab150ef6f9298e227a40c93c7fed5f3ffb67c9fb62cd49f66285e46e/authlib-1.3.2.tar.gz", hash = "sha256:4b16130117f9eb82aa6eec97f6dd4673c3f960ac0283ccdae2897ee4bc030ba2", size = 147313 }
sdist = { url = "https://files.pythonhosted.org/packages/09/47/df70ecd34fbf86d69833fe4e25bb9ecbaab995c8e49df726dd416f6bb822/authlib-1.3.1.tar.gz", hash = "sha256:7ae843f03c06c5c0debd63c9db91f9fda64fa62a42a77419fa15fbb7e7a58917", size = 146074 }
wheels = [
{ url = "https://files.pythonhosted.org/packages/df/4c/9aa0416a403d5cc80292cb030bcd2c918cce2755e314d8c1aa18656e1e12/Authlib-1.3.2-py2.py3-none-any.whl", hash = "sha256:ede026a95e9f5cdc2d4364a52103f5405e75aa156357e831ef2bfd0bc5094dfc", size = 225111 },
{ url = "https://files.pythonhosted.org/packages/87/1f/bc95e43ffb57c05b8efcc376dd55a0240bf58f47ddf5a0f92452b6457b75/Authlib-1.3.1-py2.py3-none-any.whl", hash = "sha256:d35800b973099bbadc49b42b256ecb80041ad56b7fe1216a362c7943c088f377", size = 223827 },
]
[[package]]
@ -2321,6 +2321,19 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/66/2b/a6e68d7ea6f4fbc31cce20e354d6cef484da0a9891ee6a3eaf3aa9659d01/grpcio-1.66.1-cp312-cp312-win_amd64.whl", hash = "sha256:b0aa03d240b5539648d996cc60438f128c7f46050989e35b25f5c18286c86734", size = 4275565 },
]
[[package]]
name = "grpcio-health-checking"
version = "1.62.3"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "grpcio" },
{ name = "protobuf" },
]
sdist = { url = "https://files.pythonhosted.org/packages/eb/9f/09df9b02fc8eafa3031d878c8a4674a0311293c8c6f1c942cdaeec204126/grpcio-health-checking-1.62.3.tar.gz", hash = "sha256:5074ba0ce8f0dcfe328408ec5c7551b2a835720ffd9b69dade7fa3e0dc1c7a93", size = 15640 }
wheels = [
{ url = "https://files.pythonhosted.org/packages/40/4c/ee3173906196b741ac6ba55a9788ba9ebf2cd05f91715a49b6c3bfbb9d73/grpcio_health_checking-1.62.3-py3-none-any.whl", hash = "sha256:f29da7dd144d73b4465fe48f011a91453e9ff6c8af0d449254cf80021cab3e0d", size = 18547 },
]
[[package]]
name = "grpcio-status"
version = "1.62.3"
@ -2484,7 +2497,7 @@ wheels = [
[[package]]
name = "httpx"
version = "0.27.2"
version = "0.27.0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "anyio" },
@ -2493,9 +2506,9 @@ dependencies = [
{ name = "idna" },
{ name = "sniffio" },
]
sdist = { url = "https://files.pythonhosted.org/packages/78/82/08f8c936781f67d9e6b9eeb8a0c8b4e406136ea4c3d1f89a5db71d42e0e6/httpx-0.27.2.tar.gz", hash = "sha256:f7c2be1d2f3c3c3160d441802406b206c2b76f5947b11115e6df10c6c65e66c2", size = 144189 }
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wheels = [
{ url = "https://files.pythonhosted.org/packages/56/95/9377bcb415797e44274b51d46e3249eba641711cf3348050f76ee7b15ffc/httpx-0.27.2-py3-none-any.whl", hash = "sha256:7bb2708e112d8fdd7829cd4243970f0c223274051cb35ee80c03301ee29a3df0", size = 76395 },
{ url = "https://files.pythonhosted.org/packages/41/7b/ddacf6dcebb42466abd03f368782142baa82e08fc0c1f8eaa05b4bae87d5/httpx-0.27.0-py3-none-any.whl", hash = "sha256:71d5465162c13681bff01ad59b2cc68dd838ea1f10e51574bac27103f00c91a5", size = 75590 },
]
[package.optional-dependencies]
@ -3528,7 +3541,7 @@ requires-dist = [
{ name = "celery", extras = ["redis"], marker = "extra == 'deploy'", specifier = ">=5.3.6" },
{ name = "certifi", specifier = ">=2023.11.17,<2025.0.0" },
{ name = "chromadb", specifier = ">=0.4" },
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@ -3597,7 +3610,7 @@ requires-dist = [
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{ name = "types-cachetools", specifier = ">=5.3.0.5" },
{ name = "upstash-vector", specifier = ">=0.5.0" },
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{ name = "yfinance", specifier = ">=0.2.40" },
@ -3608,7 +3621,7 @@ requires-dist = [
[package.metadata.requires-dev]
dev = [
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{ name = "httpx" },
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{ name = "mypy", specifier = ">=1.11.0" },
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@ -3697,7 +3710,6 @@ dependencies = [
{ name = "typer" },
{ name = "uncurl" },
{ name = "uvicorn" },
{ name = "websockets" },
]
[package.optional-dependencies]
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@ -3776,20 +3788,20 @@ requires-dist = [
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@ -3824,14 +3836,14 @@ requires-dist = [
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{ name = "python-jose", specifier = ">=3.3.0" },
{ name = "python-multipart", specifier = ">=0.0.7" },
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{ name = "spider-client", specifier = ">=0.0.27" },
@ -3849,7 +3861,6 @@ requires-dist = [
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{ name = "uvicorn", specifier = ">=0.30.0" },
{ name = "vulture", marker = "extra == 'dev'", specifier = ">=2.11" },
{ name = "websockets" },
]
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