apache-airflow-providers-pinecone

v2.4.5 safe
2.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risks across all categories with no signs of network or shell exploitation, low obfuscation, and no credential or metadata concerns that indicate malicious intent.

  • Low network and shell execution risks.
  • No evidence of obfuscation or credential theft.
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 the package likely does not execute system commands.
  • Obfuscation: The observed pattern is likely a standard practice for extending module paths and not indicative of malicious obfuscation.
  • Credentials: No patterns indicative of credential harvesting or secret theft were detected.
  • Metadata: The package has some minor issues but no strong indicators of malicious activity.

📦 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-pin
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (3508 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
  • 21 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-pinecone 
Create a mini-application that leverages Apache Airflow along with the 'apache-airflow-providers-pinecone' package to automate the process of indexing data into Pinecone, a vector database. Your application should have the following features:

1. A user-friendly interface where users can upload CSV files containing vectors and metadata.
2. An automated workflow using Apache Airflow that triggers when a new file is uploaded.
3. The workflow should extract the vectors and metadata from the CSV file and send them to Pinecone for indexing.
4. Include error handling within the workflow to ensure that any issues during the indexing process are logged and can be reviewed later.
5. Implement a feature to query the indexed vectors through a simple API, allowing users to search for similar vectors based on input vectors provided via the API.
6. Provide a status dashboard that shows the current state of the indexing jobs and their success/failure rates.

Use the 'apache-airflow-providers-pinecone' package to interact with Pinecone services seamlessly within your Apache Airflow DAGs. Ensure that your solution is scalable and can handle large datasets efficiently.

💬 Discussion Feed

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