apache-airflow-providers-openai

v1.7.4 safe
3.0
Low Risk

Provider package apache-airflow-providers-openai for Apache Airflow

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risks across all categories analyzed. While there are some minor concerns regarding metadata and obfuscation practices, these do not suggest any malicious activities or supply-chain attacks.

  • Low network and shell execution risks
  • Incomplete author information and insecure license link
  • Common practice observed in obfuscation risk
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external API interactions.
  • Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
  • Obfuscation: The detected pattern is likely for extending the package path using pkgutil, which is a common practice and not indicative of malicious activity.
  • Credentials: No patterns suggesting credential harvesting were detected.
  • Metadata: The author information is incomplete and the license link is not secure, but no clear signs of malicious intent are present.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 14 test file(s) found

  • Test runner config found: conftest.py
  • 14 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-ope
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (3659 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
  • 43 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)

  • under 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-openai 
Develop a fully functional mini-application using Apache Airflow along with the 'apache-airflow-providers-openai' package. This application will automate the process of fetching data from multiple sources, analyzing it with OpenAI's language models, and storing the insights in a database for further analysis or reporting. Here’s a detailed breakdown of the project steps and features:

1. **Setup**: Set up an Apache Airflow environment. Ensure you have the necessary dependencies installed, including 'apache-airflow-providers-openai'.
2. **Data Fetching DAG**: Create a Directed Acyclic Graph (DAG) in Airflow that fetches data from various sources like APIs, databases, or files. For simplicity, let's assume the data is related to customer feedback from different platforms.
3. **Data Preprocessing Task**: Add a task to your DAG that preprocesses the fetched data. This could include cleaning text, removing duplicates, or converting text into a format suitable for analysis.
4. **Sentiment Analysis Task**: Utilize the OpenAI API through the 'apache-airflow-providers-openai' package to perform sentiment analysis on the preprocessed data. The goal is to classify each piece of feedback as positive, negative, or neutral.
5. **Insight Generation Task**: Based on the sentiment analysis results, generate insights such as the overall sentiment trend, top positive and negative feedbacks, etc.
6. **Database Storage Task**: Store the analyzed data and generated insights in a relational database like PostgreSQL. Ensure that the schema is designed to efficiently store and retrieve these insights.
7. **Visualization Task**: Integrate a visualization tool (like Matplotlib or Plotly) to create graphs and charts based on the stored insights, which can then be displayed on a dashboard.
8. **Scheduling and Monitoring**: Configure Airflow to schedule the DAG to run at regular intervals (e.g., daily). Implement monitoring to ensure tasks complete successfully and handle any failures gracefully.
9. **Security and Compliance**: Ensure all interactions with external APIs, including OpenAI, comply with security best practices. Use secure methods to manage API keys and other sensitive information.

This project not only demonstrates the power of integrating Apache Airflow with advanced AI capabilities but also showcases practical applications in real-world data analysis scenarios.

💬 Discussion Feed

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