utils-b-infra 1.3.23

A collection of utility functions and classes for Python projects.

Collection of utility functions and classes designed to enhance Python projects. The library is organized into several modules, including logging, cache, translation models, client interactions, data manipulation with pandas, and general-purpose functions.

Supported Python Versions

Python >= 3.11

Installation

You can install utils-b-infra using pip:

pip install utils-b-infra

To include the translation utilities:

pip install utils-b-infra[translation]

Structure

The library is organized into the following modules:

1. logging.py: Utilities for logging with SlackAPI and writing to a file.
2. cache.py: Utilities for caching data in memory, Redis or MongoDB.
3. ai.py: Utilities for working with AI models, such as token count, tokenization, and text generation.
4. translation.py: Utilities for working with translation APIs (Supported Google Translate and DeepL).
5. services.py: Services-related utilities, such as creating google service.
6. pandas.py: Utilities for working with pandas dataframes, (df cleaning, insertion into databases...).
7. generic.py: Miscellaneous utilities that don't fit into the other specific categories (retry, run in thread, validate, etc.).

Usage

Here are few examples, for more details, please refer to the docstrings in the source code.

Logging Utilities

from utils_b_infra.logging import SlackLogger

logger = SlackLogger(project_name="your-project-name", slack_token="your-slack-token", default_channel_id="channel-id")
logger.info("This is an info message")
logger.error(exc=Exception, header_message="Header message appears above the exception message in the Slack message")

Cache Utilities

from time import sleep
from utils_b_infra.cache import Cache, CacheConfig

cache_config = CacheConfig(
   cache_type="RedisCache",
   redis_host="host",
   redis_port=6379,
   redis_password="password"
)


@cache.cached(60, namespace="test1", sliding_expiration=False)
def hello(arg1: int, arg2: str) -> dict:
   sleep(5)
   data = {
      "orders": [
         "668abd233909666c44033913",
         "668ab5167a0b54248b044b14",
         "668aad6f1cd076a89e0f4e87",
         "668ac1ff28065eadb408a9b5",
         "668ac23eb6bb7b781f069567"
      ],
      "stats": {
         "1": 10,
         "2": 22
      }
   }
   print(data)
   return data


if __name__ == "__main__":
   hello(arg1=1, arg2="test")

AI Utilities

from utils_b_infra.ai import TextGenerator
from openai import OpenAI
text_generator = TextGenerator(openai_client=OpenAI(api_key='your-openai-api-key'))

response = text_generator.generate_ai_response(
    prompt="Generate a professional email to a client based on the following text. Return JSON with 'subject' and 'body' fields.",
    user_text="Dear Client, we are pleased to inform you about our new services...",
    gpt_model='gpt-5',
    verbosity="medium",
    reasoning_effort="high",
    json_mode=True
)
print(response)

Services Utilities

from utils_b_infra.services import get_google_service

google_sheet_service = get_google_service(
   google_token_path='common/google_token.json',
   google_credentials_path='common/google_credentials.json',
   service_name='sheets'
)

Pandas Utilities

import pandas as pd
from utils_b_infra.pandas import clean_dataframe, insert_df_into_db_in_chunks

from connections import sqlalchemy_client  # Your database connection client

df = pd.read_csv("data.csv")
clean_df = clean_dataframe(df)
with sqlalchemy_client.connect() as db_connection:
    insert_df_into_db_in_chunks(
        df=clean_df,
        table_name="table_name",
        conn=db_connection,
        if_exists='append',
        truncate_table=True,
        index=False,
        dtype=None,
        chunk_size=20_000
    )

Translation Utilities To use the translation utilities, you need to install the translation extras and set up the necessary environment variables for Google Translate:

pip install utils-b-infra[translation]

import os
from utils_b_infra.translation import TextTranslator

# Set up Google Cloud credentials
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = 'path/to/google_service_account.json'

deepl_api_key = 'your-deepl-api-key'
languages = {
   'ru': 'https://ru.example.com',
   'ar': 'https://ar.example.com',
   'de': 'https://de.example.com',
   'es': 'https://es.example.com',
   'fr': 'https://fr.example.com',
   'uk': 'https://ua.example.com'
}
google_project_id = 'your-google-project-id'

translator = TextTranslator(deepl_api_key=deepl_api_key, languages=languages, google_project_id=google_project_id)

text_to_translate = "Hello, world!"
translations = translator.get_translations(
   text=text_to_translate,
   source_language="en",
   target_langs=["ru", "ar", "de"],
   engine="google"
)

for lang, translated_text in translations.items():
   print(f"{lang}: {translated_text}")

Generic Utilities

from utils_b_infra.generic import retry_with_timeout, validate_numeric_value, run_threaded, Timer


@retry_with_timeout(retries=3, timeout=5)
def fetch_data(arg1, arg2):
    # function logic here
    pass


with Timer() as t:
    fetch_data("arg1", "arg2")
print(t.seconds_taken)  # Output: Time taken to run fetch_data function (in seconds)
print(t.minutes_taken)  # Output: Time taken to run fetch_data function (in minutes)

run_threaded(fetch_data, arg1="arg1", arg2="arg2")

is_valid = validate_numeric_value(123)
print(is_valid)  # Output: True

License

This project is licensed under the MIT License. See the LICENSE file for details.

Changelog

[1.3.2] - 2025-08-22

• Add support for gpt-5 models in ai.TextGenerator class.

[1.1.0] - 2025-04-16

• Add support for processing audio files in ai.TextGenerator;

[1.0.0] - 2025-04-11

• Add support for processing files in the ai.TextGenerator class (both url and local file)
• Rename parse_json_response to json_mode in ai.TextGenerator.generate_ai_response - breaks older versions
• Rename error_additional_data to context_data in logging.SlackLogger.error() - breaks older versions
• Add support for context_data in SlackLogger.info, logging.SlackLogger.warning and SlackLogger.debug

[0.8.0] - 2025-04-01

• Add support for Slack message levels and prefixes

[0.7.0] - 2025-03-29

• Changed slack_channel_id to default_channel_id in SlackApi and SlackLogger classes.
• Updated SlackApi and SlackLogger to enable specifying custom Slack channel IDs for messages.
• Improved encapsulation in the logging classes.

[0.6.0] - 2025-02-18

• Switch the default cache database in MongoDB from the connection string to the database parameter.
• Switch the default embedding model to 'text-embedding-3-small'.
• Added support for reasoning modules with the reasoning_effort parameter in the ai.TextGenerator class.

[0.5.0] - 2024-07-30

updated

• Switch cache mechanism from async to sync

[0.4.0] - 2024-07-20

Added

• Caching modules

[0.3.0] - 2024-06-27

Changed

Split the library into main and extra modules, including optional translation utilities.

[0.2.0] - 2024-06-26:

Added

• Support for google-cloud-translate V3 API.
• Support for OpenAI modules gpt-4o and gpt-4o-2024-05-13 in ai.calculate_openai_price

Fixed

• Issue with json parsing in ai.TextGenerator.get_ai_response.

Changed

• Default openai model to gpt-4o in ai.TextGenerator.get_ai_response.
• Updated Readme file with more examples.

[0.1.0] - 2024-06-25 initial release

Added

• Initial release of the package.