apache-airflow-providers-common-ai

v0.3.0 safe
3.0
Low Risk

Provider package apache-airflow-providers-common-ai for Apache Airflow

🤖 AI Analysis

Final verdict: SAFE

The package shows low risks across all categories except metadata, where it has a non-secure external link and limited author information. These factors do not strongly suggest a supply-chain attack.

  • No network calls or shell executions detected
  • Low credential and obfuscation risks
  • Metadata concerns but no strong indicators of malicious intent
Per-check LLM notes
  • Network: No network calls detected, which is normal and expected for a package that does not require external API interactions.
  • Shell: No shell execution patterns detected, indicating the package does not execute system commands, which is typical for a library focused on providing functionality rather than system management.
  • Obfuscation: The observed pattern is likely related to extending the module's path and does not indicate malicious obfuscation.
  • Credentials: No patterns indicative of credential harvesting were detected.
  • Metadata: The package has a non-secure external link and an author with limited information, which may indicate a less active or new maintainer.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 16 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: conftest.py
  • 16 test file(s) detected (e.g. conftest.py)
✦ High Documentation 9.0

Well-documented package

  • Documentation URL: "Documentation" -> https://airflow.apache.org/docs/apache-airflow-providers-com
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (4861 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 125 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 46 unique contributor(s) across 100 commits in apache/airflow
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • nder the License. __path__ = __import__("pkgutil").extend_path(__path__, __name__) # Licensed to the Apache S
✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: airflow.apache.org>

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://www.apache.org/licenses/LICENSE-2.0
✓ Git Repository History

Repository apache/airflow appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with apache-airflow-providers-common-ai 
Create a data pipeline automation tool using Apache Airflow and the 'apache-airflow-providers-common-ai' package. This tool will automate the process of fetching data from a common AI service, processing it, and then storing it in a database for further analysis. Here’s a detailed plan on how to build this application:

1. **Project Setup**: Start by setting up your development environment. Ensure you have Python installed along with pip. Next, install Apache Airflow and the 'apache-airflow-providers-common-ai' package. Additionally, set up a local or cloud-based PostgreSQL database for storing processed data.

2. **Data Fetching Task**: Use the 'apache-airflow-providers-common-ai' package to create a custom operator that fetches data from a common AI service (e.g., sentiment analysis from a text dataset). This operator should handle authentication and API requests efficiently.

3. **Data Processing Task**: Develop a task that processes the fetched data. This could involve cleaning the data, transforming it into a suitable format for storage, and applying any necessary preprocessing steps like normalization or feature extraction.

4. **Data Storage Task**: Implement a task that stores the processed data into the PostgreSQL database. Ensure the schema is designed to optimize data retrieval for future analysis tasks.

5. **Visualization Task**: Create a simple visualization task that generates charts or graphs based on the stored data, providing insights into the processed information. This can be achieved using libraries such as Matplotlib or Plotly.

6. **DAG Creation**: Organize all these tasks into Directed Acyclic Graphs (DAGs) within Apache Airflow. Define dependencies between tasks to ensure they run in the correct order.

7. **Testing & Deployment**: Test the entire pipeline locally to ensure all tasks execute as expected. Once tested, deploy the application to a cloud platform like AWS or GCP to make it accessible and scalable.

8. **Documentation & User Guide**: Provide comprehensive documentation detailing how to set up the environment, run the pipeline, and interpret the results. Include screenshots, code snippets, and troubleshooting tips.

This project not only showcases the power of Apache Airflow for automating complex workflows but also demonstrates the integration of third-party services through the 'apache-airflow-providers-common-ai' package, making it a valuable tool for data scientists and engineers.

💬 Discussion Feed

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