apache-airflow-providers-qdrant

v1.5.5 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package has low risks across all categories, with only minor issues related to metadata完整性很重要，以下是完整的JSON响应，确保所有字段都被正确填充并符合要求格式：监视提供的信息和问题描述，并且保持回答仅在JSON格式中。我将根据给定的信息生成一个合理的评估结果，确保与原始问题相匹配的同时，提供一个简洁而全面的回答。以下是基于提供的信息生成的JSON格式的答案示例：

  • Low network and shell risks
  • Potential for standard obfuscation techniques
  • Missing author name and non-HTTPS link in metadata
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution detected, which is expected and indicates no immediate risk of command injection or similar attacks.
  • Obfuscation: The detected pattern is likely a standard method for extending package paths and does not indicate malicious obfuscation.
  • Credentials: No patterns indicative of credential harvesting were found.
  • Metadata: The package shows some red flags such as a missing author name and a non-HTTPS external link, but no clear signs of typosquatting or other malicious intent.

📦 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
  • 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-qdr
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (3488 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
  • 3 type-annotated function signatures (partial)
✦ 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-qdrant 
Create a data processing pipeline using Apache Airflow and the 'apache-airflow-providers-qdrant' package. This pipeline will ingest data from various sources, process it, and then store it in Qdrant, a vector database designed for similarity search. Your task is to design a fully-functional mini-application that demonstrates the integration of these technologies.

Steps to follow:
1. Set up your development environment with Python, Apache Airflow, and Qdrant.
2. Define the DAG (Directed Acyclic Graph) structure for your workflow within Airflow.
3. Use the 'apache-airflow-providers-qdrant' package to interact with Qdrant from within your DAG tasks.
4. Implement tasks to simulate data ingestion from different sources (e.g., CSV files, APIs).
5. Add data processing tasks that transform the ingested data into vectors suitable for storage in Qdrant.
6. Finally, create tasks that insert these vectors into Qdrant and perform similarity searches.
7. Ensure your pipeline includes error handling and retries for failed tasks.
8. Document your setup and workflow clearly, explaining each step and decision made during implementation.

Suggested Features:
- Task scheduling and execution control within Airflow.
- Dynamic data ingestion from multiple sources.
- Vectorization of textual data using pre-trained models like BERT.
- Storage and retrieval of vectors in Qdrant.
- Implementation of a simple UI to visualize search results from Qdrant.

How 'apache-airflow-providers-qdrant' is Utilized:
- The package provides operators and hooks that facilitate communication between Airflow and Qdrant, allowing you to seamlessly integrate data processing and storage tasks into your pipeline without needing to manually handle API requests or configurations.

💬 Discussion Feed

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