Python Backend Code

The Python Backend Code step allows you to write Python code that will be run on the backend. The common use cases for the action step are data transforming and mapping, as well as execution of machine learning-related functions.

You can use a number of pre-installed libraries to utilize their functionality within your code:

    abc,
    array,
    asyncio,
    azure-cosmos,
    base64,
    bs4,
    bson,
    builtins,
    collections,
    charset_normalizer,
    dateutil,
    errno,
    geopy,
    google.protobuf,
    gc,
    jinja2,
    jira,
    jwt,
    machine,
    math,
    matplotlib,
    music,
    neopixel,
    numpy,
    openai,
    packaging,
    pandas,
    power,
    pypdf,
    pytz,
    query7,
    radio,
    random,
    requests,
    rsa,
    scipy,
    seaborn,
    sklearn,
    speech,
    struct,
    tabulate,
    time,
    types,
    typeshed,
    typing,
    typing_extensions,
    uarray,
    ucollections,
    uerrno,
    urandom,
    ustructu,
    usys,
    utime,
    yaml,
    datetime,
    llama_index,
    tiktoken

Variables in the code

If you want to use app variables in the Python code step, you must add them to the Variables section and NOT use directly in the code.

There, you can pass values from UI components using {{ui.input.value}}. You can also use some predefined variables to access the result of the previous step or to get user-related information:

# result of the previous step
return data

# error response of the previous step
return error

# incoming action params, passed in by components,
# the Execution/Loop action steps or when calling the action from the code
return params

# get roles of the current user
return user.get('roles')

While data and error are specific to a particular step, params is available in all steps.

Merging results of multiple steps

In some cases, you may need to merge the results of multiple steps into a single object. This must also be done using the Variables section - pass this information to Python and access the output of any previous step using {{steps.<step_name>.data}}.

Importing external modules

To import external libraries, such as NumPy or Pandas, you can use the import statement in your Python code. These libraries provide a wide range of useful functions and tools for data manipulation and analysis.

Here's an example of how to import NumPy:

import numpy as np

# Now you can use NumPy functions and tools in your code, for example:
my_array = np.array([1, 2, 3])

Similarly, here's an example of how to import Pandas:

import pandas as pd

# Now you can use Pandas functions and tools in your code, for example:
my_dataframe = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})

Data transformation

If the API returns its data in a different format than expected for the components, you can use the Python Backend Code step to transform it. Python offers several built-in methods for transforming data, such as map(), filter(), and reduce(). For example, to add a new key to a list of dictionaries you can use the following code:

data = [{'name': 'Alice', 'age': 25}, {'name': 'Bob', 'age': 30}]
new_data = [{'name': d['name'], 'age': d['age'], 'is_adult': d['age'] >= 18} for d in data]

This creates a new list of dictionaries with an additional key is_adult that is true if the age key is greater than or equal to 18. You can also use functions from external libraries such as NumPy and Pandas for more complex transformations.

Debugging errors

In case the code is failing or produces unexpected results:

• Make sure no linter errors are present in the code (exclamation mark in the left gutter).

• Check the result of the previous step (the data variable) and of other variables (for example, params).

• Comment out the code and add return data to see if the data is in the expected format.

• Use print to print the data to the console (Logs tab at the bottom of the action):

# commented to test
# if length(data) > 10:
#   return data
# 

print(data)