ai-prishtina-agentic-rag

v1.0.4 suspicious
7.0
High Risk

A comprehensive, professional-grade agentic Retrieval-Augmented Generation (RAG) library — core building blocks for building RAG applications

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits a high shell risk and moderate network and obfuscation risks, indicating potential vulnerabilities and hidden intentions. The missing repository and incomplete maintainer information further increase suspicion.

  • High shell risk due to potential for executing unsanitized commands
  • Missing repository and incomplete maintainer information
Per-check LLM notes
  • Network: Network calls are common in many packages and might be used for legitimate purposes such as API interactions.
  • Shell: Executing shell commands can pose significant risks if not properly sanitized or controlled, suggesting potential for misuse or unintended consequences.
  • Obfuscation: The use of eval and obfuscated code patterns suggests potential for malicious intent or hiding code logic, but it could also be part of complex functionality like model evaluation in machine learning.
  • Credentials: No clear signs of credential harvesting detected.
  • Metadata: The repository is not found and the maintainer's information is incomplete, raising suspicion.

📦 Package Quality Overall: Medium (5.0/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://ai-prishtina-agentic-rag.readthedocs.io
  • Detailed PyPI description (103354 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
  • 419 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

  • s async with aiohttp.ClientSession() as session: async with session.post(url, heade
  • ] async with aiohttp.ClientSession() as session: async with session.post(self.webho
  • } async with aiohttp.ClientSession() as session: async with session.get(url, header
  • s async with aiohttp.ClientSession() as session: async with session.post(self.webho
⚠ Code Obfuscation score 8.0

Found 4 obfuscation pattern(s)

  • result = eval(expression) return ToolResult(success=Tr
  • self._model.eval() self.logger.info(f"Loaded classifier model on
  • import torch model.eval() model.qconfig = torch.quantization.get_default_qco
  • if isinstance(module, __import__("torch").nn.Linear): prune.l1_unstructured(module, n
⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • try: result = subprocess.run( cmd, cwd=work_dir,
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: albanmaxhuni.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ 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 ai-prishtina-agentic-rag 
Create a personalized news summarization app using the 'ai-prishtina-agentic-rag' package. This app will fetch the latest news articles from various sources, summarize them using retrieval-augmented generation techniques, and present them in a concise and digestible format tailored to each user's interests. Here’s how you can build it step-by-step:

1. **User Onboarding**: Allow users to sign up/log in and specify their areas of interest (e.g., technology, sports, politics).
2. **News Fetching**: Integrate the app with popular news APIs to fetch the latest articles.
3. **Summarization Engine**: Use 'ai-prishtina-agentic-rag' to process and summarize these articles. The engine should extract key points, ensuring the summaries are coherent and relevant to the user's interests.
4. **Personalized Recommendations**: Based on past interactions and preferences, the app should recommend similar articles to keep users engaged.
5. **User Interface**: Develop a clean, user-friendly interface where users can view summaries, read full articles, and adjust their preferences.
6. **Feedback Loop**: Implement a feature allowing users to rate the relevance and quality of summaries, which will help improve the model over time.
7. **Accessibility Features**: Ensure the app supports accessibility options such as text-to-speech for summaries.

The 'ai-prishtina-agentic-rag' package will be central to the summarization process. It provides the necessary tools for fetching, processing, and generating summaries that are both accurate and engaging. Additionally, its agentic capabilities allow for continuous learning and improvement based on user interaction.