ai-parrot-loaders

v0.1.61 suspicious
6.0
Medium Risk

Document loaders for AI-Parrot RAG pipelines

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to potential shell execution and network calls, which could be exploited in a supply-chain attack.

  • High shell risk indicating potential for executing arbitrary commands
  • Moderate network risk suggesting possible uncontrolled HTTP requests
Per-check LLM notes
  • Network: The network call pattern suggests the package may be making HTTP requests, which is not inherently suspicious but should be reviewed based on the package's intended functionality.
  • Shell: The shell execution patterns indicate that the package could potentially execute arbitrary commands on the user's system, which poses a significant risk if not properly sanitized and controlled.
  • Obfuscation: The obfuscation pattern appears to be related to code formatting and might not indicate malicious activity.
  • Credentials: No credentials or secrets harvesting patterns were detected.
  • Metadata: The author information is incomplete and the maintainer has a single package, which may indicate a new or less active account.

📦 Package Quality Overall: Medium (7.0/10)

✦ High Test Suite 9.0

Test suite present — 5 test file(s) found

  • 5 test file(s) detected (e.g. test_base_loader_helpers.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/phenobarbital/ai-parrot/
  • Detailed PyPI description (2567 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

  • Classifier: Typing :: Typed
  • 268 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 100 commits in phenobarbital/ai-parrot
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • headers() async with aiohttp.ClientSession(headers=headers) as session: while page_count <
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • ._device) self._model.eval() self.logger.info( "LayoutLMv3 model l
⚠ Shell / Subprocess Execution score 8.0

Found 4 shell execution pattern(s)

  • ] result = subprocess.run(command, check=True, stdout=subprocess.PIPE, text=True)
  • ] result = subprocess.run( command, check=True
  • s") ] subprocess.run(command, check=True) return file_path ex
  • '.join(dl_cmd)}") subprocess.run( dl_cmd, check=True,
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: phenobarbital.info>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository phenobarbital/ai-parrot 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 ai-parrot-loaders 
Create a mini-application named 'DocSummarizer' that leverages the 'ai-parrot-loaders' Python package to efficiently load and summarize documents using Retrieval-Augmented Generation (RAG) techniques. Your goal is to develop a tool that allows users to upload various types of documents (PDFs, Word documents, etc.), process them through an AI-powered summarization pipeline, and then present key insights or summaries back to the user.

Step 1: Set up your development environment by installing Python and necessary packages including 'ai-parrot-loaders', 'transformers', and any other dependencies needed for document processing and summarization.

Step 2: Design the user interface where users can select and upload their documents. Ensure the application supports multiple file formats commonly used for storing documents such as .pdf, .docx, and .txt.

Step 3: Implement a document loader using 'ai-parrot-loaders'. This module will handle the extraction of text content from uploaded files. It should be able to parse different file types and return the textual data ready for further processing.

Step 4: Develop a summarization engine that takes the extracted text and generates a concise summary. Use pre-trained models from 'transformers' library for this task. Consider implementing options for adjusting the level of detail in the summary based on user preference.

Step 5: Integrate error handling mechanisms to manage cases where the input document is not supported or if there are issues during the summarization process.

Suggested Features:
- Support for multiple file formats.
- User-friendly interface for uploading documents.
- Adjustable summary length based on user preference.
- Error messages for unsupported file types or summarization failures.
- Option to save the generated summary as a text file.

The 'ai-parrot-loaders' package plays a crucial role in Step 3 by providing robust document loading capabilities. By utilizing its features, you ensure that the application can handle a variety of document types efficiently, making it versatile and useful for a wide range of users.