brownllama 0.1.24

Standard PIP Package for GCP integration apps.

brownLlama Pip Package

Overview

brownllama-pip (or brownllama when imported in Python) is a utility library designed to provide common functionalities and standardized components for VNP's Python projects. It aims to reduce code duplication and promote best practices across different projects.

Modules

The package currently includes the following modules:

• bigquery.py:

  • Provides the BigQueryController class for simplified interaction with Google BigQuery.
  • Supports creating tables, checking table existence, loading data from JSON, loading data from Google Cloud Storage (GCS), and executing queries.
  • Offers methods for schema inference from JSON data and efficient data loading via GCS staging.

• logger.py:

  • Provides the get_logger function for obtaining configured logger instances.
  • Standardizes logging format and setup across projects.
  • Configures both root logger and common third-party library loggers to ensure consistent logging behavior.

• secret_manager.py

  • Provides the get_secret function for retrieving secrets from Google Secret Manager.
  • Enables to get, create, delete and lists the secrets.

• storage.py:

  • Provides the StorageManager class for managing Google Cloud Storage operations.
  • Supports uploading data (dictionaries, lists of dictionaries, Pandas DataFrames) to GCS as JSON files.
  • Includes functionality for deleting files from GCS.

Building and Publishing

To build and publish the package, follow these steps:

1. Install build dependencies:

   uv add build twine
   

2. Change the version number in pyproject.toml file

   version = "0.1.XXX"
   

3. Build the package:

   uv build
   

4. Publish the package in PyPI

   uvx twine upload --verbose dist/*
   

   NOTE: PyPI is public and should not be used for sensitive information. Also, you need to setup ~/.pypirc with your PyPI credentials.

Usage

After installation, you can import and use the modules and classes provided by brownllama in your Python projects.

Example (using BigQueryController):

from brownllama.bigquery import BigQueryController


bq_controller = BigQueryController(bigquery_payload=bigquery_payload, key_path=key_path)

# Example: Export JSON data to BigQuery via GCS
gcs_uri = bq_controller.export_to_bq_via_gcs(json_data=data)
print(f"Data loaded to BigQuery via GCS: {gcs_uri}")

Refer to the individual module files (bigquery.py, logger.py, storage.py) for detailed class and function documentation and usage examples.