🤖 AI Analysis

Final verdict: SUSPICIOUS

The package appears to have legitimate functionality as an inference layer for another package, but its recent creation and limited activity make it suspicious. Further investigation is recommended.

recently created with limited activity
network communication with external endpoints

Per-check LLM notes

Network: The network calls suggest the package is designed to communicate with external endpoints, possibly for API-based services like chat or embeddings.
Shell: No shell execution patterns detected, indicating no immediate risk associated with command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package is newly created with limited activity, which raises some suspicion but does not conclusively indicate malice.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

try: response = requests.post( self._chat_endpoint(), head
try: response = requests.post( self._endpoint(), headers=s
try: response = requests.post( f"{self.base_url}/embeddings",

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Only one version has ever been released — brand new package
Author "Juntao Zhang" 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 actllminfer

Create a versatile document summarization tool using the 'actllminfer' package. This tool will allow users to input various types of documents (e.g., PDFs, Word documents, plain text files) and receive a concise summary of their content. The application should support multiple languages and utilize different LLM providers through the 'actllminfer' package for inference. Here’s a step-by-step guide on how to build this tool:

1. **Setup Environment**: Install Python and the necessary libraries including 'actllminfer', 'pandas', 'langchain', 'PyPDF2', 'python-docx', etc.
2. **User Interface Design**: Develop a simple web interface using Flask or Streamlit where users can upload their documents.
3. **Document Processing**: Implement functions to read and parse different file formats into plain text. Use PyPDF2 for PDFs and python-docx for Word documents.
4. **LLM Integration**: Utilize 'actllminfer' to integrate with various LLM providers such as Kimi, GLM, MiniMax, DeepSeek, OpenAI, Anthropic, Hugging Face, and NVIDIA NIM. Configure settings within 'actllminfer' to select preferred models and languages.
5. **Summarization Functionality**: Create a function that takes parsed text as input and returns a summarized version. This function should leverage 'actllminfer' to call the selected LLM provider's API and process the response.
6. **Error Handling & Logging**: Implement robust error handling mechanisms to manage issues like unsupported file formats, connection errors with LLM APIs, etc. Log these events for future analysis.
7. **Testing**: Thoroughly test the application with a variety of documents and languages to ensure accuracy and reliability of summaries.
8. **Deployment**: Deploy the application on a cloud service like AWS, Azure, or Google Cloud Platform.

Suggested Features:
- Support for multiple languages.
- User-friendly interface for uploading files.
- Real-time status updates while processing documents.
- Option to choose between different LLM providers for summarization.
- Detailed logs for troubleshooting.

By following these steps and utilizing the 'actllminfer' package effectively, you can create a powerful yet accessible document summarization tool.