Python Interpreter

Estimated reading: 3 minutes 109 views

This component allows you to execute Python code with imported packages.

The Python Interpreter component can only import packages that are already installed in your Robility flow environment. If you encounter an ImportError when trying to use a package, you need to install it first.

Use the Python Interpreter in a flow

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. At least one import is required.
2. In the Python Code field, enter the Python code you want to execute. Use print() to see the output.

Optional: Enable Tool Mode and then connect the Python Interpreter component to an Agent component as a tool. For example, connect a Python Interpreter component and a Calculator component as tools for an Agent component, and then test how it chooses different tools to solve math problems.

4. Ask the agent an easier math question. The Calculator tool can add, subtract, multiple, divide, or perform exponentiation. The agent executes the evaluate_expression tool to correctly answer the question.

Result:

Executed evaluate_expression

Input:

{

“expression”: “2+5”

}

Output:

{

“result”: “7”

}

5. Give the agent complete Python code. This example creates a Pandas DataFrame table with imported pandas packages, and returns the square root of the mean squares.

import pandas as pd

import math

# Create a simple DataFrame

df = pd.DataFrame({

‘numbers’: [1, 2, 3, 4, 5],

‘squares’: [x**2 for x in range(1, 6)]

})

# Calculate the square root of the mean

result = math.sqrt(df[‘squares’].mean())

print(f”Square root of mean squares: {result}”)

The agent correctly chooses the run_python_repl tool to solve the problem.

Result:

Executed run_python_repl

Input:

{

“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}\”)”

}

Output:

{

“result”: “Square root of mean squares: 3.3166247903554”

}

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.