atoti-client-ai-amazon-bedrock

v0.9.15 suspicious
4.0
Medium Risk

Experimental plugin to use Amazon Bedrock AI with Atoti

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low individual risks in terms of network, shell, obfuscation, and credential handling. However, the metadata risk score is elevated due to the maintainer having only one package, which raises suspicion about the legitimacy and stability of the package.

  • Metadata risk score of 3/10 due to a single package from the maintainer
  • No description provided for the package
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require internet access.
  • Shell: No shell execution patterns detected, indicating no direct system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets and credentials.
  • Metadata: The maintainer has only one package, which may indicate a new or less active account, but there are no other suspicious flags.

📦 Package Quality Overall: Low (4.2/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Documentation URL: "Documentation" -> https://docs.activeviam.com/products/atoti/python-sdk/0.9.15
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 100 commits in atoti/atoti
  • Two distinct contributors found

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ 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

Email domain looks legitimate: activeviam.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository atoti/atoti appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "ActiveViam" 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 atoti-client-ai-amazon-bedrock 
Create a mini-application that leverages the 'atoti-client-ai-amazon-bedrock' package to integrate Amazon Bedrock AI services into an interactive data analytics dashboard. The application will allow users to upload datasets, perform exploratory data analysis (EDA), and generate insights using Amazon Bedrock's AI capabilities. Here’s a detailed breakdown of the steps and features:

1. **Setup Environment**: Ensure you have Python installed along with the necessary packages including 'atoti', 'pandas', and 'atoti-client-ai-amazon-bedrock'. Set up your Amazon Bedrock credentials securely.

2. **Data Import**: Develop a user-friendly interface where users can upload their CSV or Excel files. Validate the file format and handle common errors such as missing headers or incorrect formats.

3. **Data Exploration**: Implement basic EDA tools within the dashboard. This includes visualizing distributions, correlations, and summary statistics of the dataset. Use Atoti's capabilities to dynamically update these visualizations based on user interactions.

4. **AI-Powered Insights**: Utilize the 'atoti-client-ai-amazon-bedrock' package to connect with Amazon Bedrock AI services. Integrate AI models from Bedrock to automatically analyze the uploaded data, generating insights such as trend predictions, anomaly detection, and sentiment analysis for textual data. Display these insights alongside the raw data and EDA results.

5. **Customizable Dashboards**: Allow users to customize their dashboards by selecting which metrics and visualizations they want to display. Save these customizations for future sessions.

6. **Export Functionality**: Provide options for users to export their analyzed data and generated reports in various formats like PDF, Excel, or CSV.

7. **Security & Privacy**: Ensure all data handling complies with GDPR and other relevant data protection regulations. Use secure methods to store and transmit data.

8. **Documentation & Support**: Create comprehensive documentation detailing how to install, configure, and use the application. Include troubleshooting guides and FAQs.

By following these steps, you'll create a powerful tool that not only helps users understand their data better but also provides advanced AI-driven insights without requiring deep technical knowledge.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!