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* fix: nightly now properly gets 1.9.0 branch (#12215) before it was attempting to pull release-notes as letters are alphanumerically after numbers when we sort -V then grab tail now we only look at branch names that follow the pattern '^release-[0-9]+\.[0-9]+\.[0-9]+$' * docs: add search icon (#12216) add-back-svg * initial-content * cut-1.8-release-and-include-next-version * stage-1.8.0-and-next --------- Co-authored-by: Adam-Aghili <149833988+Adam-Aghili@users.noreply.github.com>
239 lines
10 KiB
Plaintext
239 lines
10 KiB
Plaintext
---
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title: Python Interpreter
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slug: /python-interpreter
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---
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import Icon from "@site/src/components/icon";
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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This component allows you to execute Python code with imported packages.
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The **Python Interpreter** component can only import packages that are already installed in your Langflow environment.
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If you encounter an `ImportError` when trying to use a package, you need to install it first.
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To install custom packages, see [Install custom dependencies](/install-custom-dependencies).
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## Use the Python Interpreter in a flow
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1. To use this component in a flow, in the **Global Imports** field, add the packages you want to import as a comma-separated list, such as `math,pandas`.
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At least one import is required.
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2. In the **Python Code** field, enter the Python code you want to execute. Use `print()` to see the output.
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3. Optional: Enable **Tool Mode**, and then connect the **Python Interpreter** component to an **Agent** component as a tool.
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For example, connect a **Python Interpreter** component and a [**Calculator** component](/calculator) as tools for an **Agent** component, and then test how it chooses different tools to solve math problems.
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4. Ask the agent an easier math question.
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The **Calculator** tool can add, subtract, multiple, divide, or perform exponentiation.
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The agent executes the `evaluate_expression` tool to correctly answer the question.
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Result:
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```text
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Executed evaluate_expression
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Input:
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{
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"expression": "2+5"
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}
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Output:
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{
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"result": "7"
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}
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```
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5. Give the agent complete Python code.
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This example creates a Pandas DataFrame table with the imported `pandas` packages, and returns the square root of the mean squares.
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```python
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import pandas as pd
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import math
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# Create a simple DataFrame
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df = pd.DataFrame({
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'numbers': [1, 2, 3, 4, 5],
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'squares': [x**2 for x in range(1, 6)]
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})
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# Calculate the square root of the mean
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result = math.sqrt(df['squares'].mean())
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print(f"Square root of mean squares: {result}")
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```
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The agent correctly chooses the `run_python_repl` tool to solve the problem.
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Result:
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```text
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Executed run_python_repl
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Input:
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{
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"python_code": "import pandas as pd\nimport math\n\n# Create a simple DataFrame\ndf = pd.DataFrame({\n 'numbers': [1, 2, 3, 4, 5],\n 'squares': [x**2 for x in range(1, 6)]\n})\n\n# Calculate the square root of the mean\nresult = math.sqrt(df['squares'].mean())\nprint(f\"Square root of mean squares: {result}\")"
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}
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Output:
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{
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"result": "Square root of mean squares: 3.3166247903554"
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}
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```
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If you don't include the package imports in the chat, the agent can still create the table using `pd.DataFrame`, because the `pandas` package is imported globally by the **Python Interpreter** component in the **Global Imports** field.
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## Pass inputs to the Python Interpreter
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To pass inputs to the **Python Interpreter** component, you need to customize the component's code to add input fields.
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After the input field is added to the component code, the port becomes available for connections.
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For example, to connect a [**Text** component](/text-input-and-output) and pass a URL value to the **Python Interpreter** component, do the following:
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1. Add a **Python Interpreter** component to your flow.
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2. To modify the **Python Interpreter** component's code, click <Icon name="Code" aria-hidden="true"/> **Edit Code**.
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3. To pass a URL input to the **Python Interpreter** component, make the following changes to the code:
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a. Add the URL input field to the `inputs` list. This creates the input port that other components can connect to.
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b. Update the `get_globals` method to extract the URL value and add it to the globals dictionary.
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This makes the `url` variable available in the component's Python code.
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c. Update the default Python code value to use the `url` variable.
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The following example demonstrates these modifications.
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<details open>
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<summary>Python code example</summary>
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```python
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import importlib
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from langchain_experimental.utilities import PythonREPL
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from lfx.custom.custom_component.component import Component
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from lfx.io import MultilineInput, Output, StrInput
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from lfx.schema.data import Data
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from lfx.schema.message import Message # Needed to extract text from Message objects
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class PythonREPLComponent(Component):
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display_name = "Python Interpreter"
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description = "Run Python code with optional imports. Use print() to see the output."
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documentation: str = "https://docs.langflow.org/python-interpreter"
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icon = "square-terminal"
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inputs = [
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StrInput(
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name="global_imports",
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display_name="Global Imports",
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info="A comma-separated list of modules to import globally, e.g. 'math,numpy,pandas'.",
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value="math,pandas",
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required=True,
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),
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MultilineInput(
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name="python_code",
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display_name="Python Code",
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info="The Python code to execute. Only modules specified in Global Imports can be used. Use 'url' variable if URL input is connected.",
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value="print(f'URL: {url}')", # Updated to make the URL variable available to the Python code execution
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input_types=["Message"],
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tool_mode=True,
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required=True,
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),
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# Add the URL input field to inputs list
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StrInput(
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name="url",
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display_name="URL",
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info="URL variable that can be used in Python code. Connect a Text component or enter manually.",
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value="",
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input_types=["Text", "Message"],
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required=False,
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),
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]
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outputs = [
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Output(
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display_name="Results",
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name="results",
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type_=Data,
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method="run_python_repl",
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),
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]
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def get_globals(self, global_imports: str | list[str]) -> dict:
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"""Create a globals dictionary with only the specified allowed imports and input variables."""
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global_dict = {}
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try:
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if isinstance(global_imports, str):
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modules = [module.strip() for module in global_imports.split(",")]
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elif isinstance(global_imports, list):
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modules = global_imports
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else:
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msg = "global_imports must be either a string or a list"
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raise TypeError(msg)
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for module in modules:
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try:
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imported_module = importlib.import_module(module)
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global_dict[imported_module.__name__] = imported_module
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except ImportError as e:
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msg = f"Could not import module {module}: {e!s}"
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raise ImportError(msg) from e
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# Add the URL variable to the component's globals dictionary
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# Extract from Message object or use the string directly
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if hasattr(self, "url") and self.url:
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url_value = self.url.text if isinstance(self.url, Message) else str(self.url)
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if url_value:
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global_dict["url"] = url_value # Makes 'url' available in Python code
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self.log(f"URL variable set: {url_value}")
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except Exception as e:
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self.log(f"Error in global imports: {e!s}")
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raise
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else:
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self.log(f"Successfully imported modules: {list(global_dict.keys())}")
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return global_dict
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def run_python_repl(self) -> Data:
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try:
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# Extract Python code text if it's a Message object
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python_code_text = self.python_code
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if isinstance(python_code_text, Message):
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python_code_text = python_code_text.text if python_code_text.text else ""
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elif not isinstance(python_code_text, str):
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python_code_text = str(python_code_text)
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globals_ = self.get_globals(self.global_imports)
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python_repl = PythonREPL(_globals=globals_)
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result = python_repl.run(python_code_text)
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result = result.strip() if result else ""
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self.log("Code execution completed successfully")
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return Data(data={"result": result})
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except ImportError as e:
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error_message = f"Import Error: {e!s}"
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self.log(error_message)
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return Data(data={"error": error_message})
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except SyntaxError as e:
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error_message = f"Syntax Error: {e!s}"
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self.log(error_message)
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return Data(data={"error": error_message})
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except (NameError, TypeError, ValueError) as e:
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error_message = f"Error during execution: {e!s}"
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self.log(error_message)
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return Data(data={"error": error_message})
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def build(self):
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return self.run_python_repl
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```
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</details>
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4. To save the modifications, click **Check & Save**.
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5. Add a **Text** component to your flow and set its value, such as `google.com`.
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6. Connect the **Text** component's output to the new **URL** input field on your customized **Python Interpreter** component.
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The **Python Interpreter** component can now use the `url` variable in the Python code that it executes.
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## Python Interpreter parameters
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| Name | Type | Description |
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|------|------|-------------|
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| global_imports | String | Input parameter. A comma-separated list of modules to import globally, such as `math,pandas,numpy`. |
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| python_code | Code | Input parameter. The Python code to execute. Only modules specified in Global Imports can be used. |
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| results | Data | Output parameter. The output of the executed Python code, including any printed results or errors. | |