apache-airflow-providers-weaviate

v3.3.4 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package shows low risks across all categories with no evidence of malicious activities. The metadata risk is slightly elevated due to a non-secure link and limited author details, but there's no indication of a supply-chain attack.

  • Low network and shell execution risks
  • No signs of obfuscation or credential harvesting
  • Metadata risk is minor
Per-check LLM notes
  • Network: No network call patterns detected, which is normal for a package that does not require external API interactions.
  • Shell: No shell execution patterns detected, which is expected for a standard Python package without system command requirements.
  • Obfuscation: The observed pattern is likely a standard method for extending package paths and does not indicate malicious obfuscation.
  • Credentials: No suspicious patterns related to credential harvesting were detected.
  • Metadata: The package has a non-secure link and an author with minimal information, but no clear signs of malicious intent.

📦 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-wea
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (3989 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
  • 37 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-weaviate 
Create a mini-application using Apache Airflow and the 'apache-airflow-providers-weaviate' package to manage data ingestion workflows into a Weaviate instance. Your application should automate the process of fetching data from various sources (such as APIs, databases, or flat files), transforming it as necessary, and loading it into a Weaviate instance for semantic search and analysis purposes. Here are the steps and features your application should include:

1. **DAG Creation**: Define Directed Acyclic Graphs (DAGs) in Airflow to represent the workflow of data ingestion. Each DAG will have tasks for data extraction, transformation, and loading (ETL).
2. **Data Sources Integration**: Implement tasks within your DAGs that can fetch data from different sources such as REST APIs, SQL databases, or CSV files. Ensure you handle authentication and error handling gracefully.
3. **Transformation Logic**: Develop transformation tasks that clean, normalize, and enrich the fetched data before loading it into Weaviate. This could involve operations like type conversion, filtering, or adding metadata.
4. **Weaviate Interaction**: Use the 'apache-airflow-providers-weaviate' package to interact with a Weaviate instance. This includes defining schemas, inserting data, and optionally querying data back from Weaviate to verify the integrity of the loaded data.
5. **Scheduling & Monitoring**: Set up scheduling for your DAGs to run at regular intervals (e.g., daily, hourly). Additionally, implement monitoring to track the status of each task and alert on failures.
6. **Documentation & Testing**: Provide comprehensive documentation detailing how to set up and run your application. Include unit tests for your ETL processes and integration tests with the Weaviate instance to ensure everything works as expected.
7. **Optional Enhancements**: Consider adding features like versioning of data in Weaviate, support for multiple Weaviate instances, or implementing a retry mechanism for failed tasks.

Your goal is to create a reusable and maintainable mini-application that showcases the capabilities of both Apache Airflow and the 'apache-airflow-providers-weaviate' package in managing complex data workflows.

💬 Discussion Feed

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