atoti-server-ai-amazon-bedrock

v0.9.15 suspicious
4.0
Medium Risk

Resources to use Amazon Bedrock AI with Atoti

⚠ Tarball exceeded 25 MB — source code analysis was limited to package metadata only.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal risk indicators with no network calls, shell executions, or obfuscation. However, the maintainer's single package raises a concern about potential new or less active accounts, warranting further scrutiny.

  • Maintainer has only one package
  • No description provided for the package
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external API interactions.
  • Shell: No shell execution patterns detected, indicating the package does not execute system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of code obfuscation for malicious purposes.
  • Credentials: No credential harvesting patterns detected, indicating low risk of unauthorized credential access.
  • Metadata: The maintainer has only one package, which could indicate a new or less active account, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (3.4/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
○ Low Documentation 1.0

No documentation detected

  • No documentation URL, doc files, or meaningful description found
○ 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-server-ai-amazon-bedrock 
Create a data analytics dashboard using Python that integrates Amazon Bedrock AI services through the 'atoti-server-ai-amazon-bedrock' package. This dashboard will serve as a tool for businesses to analyze their customer feedback data in real-time, providing insights on sentiment analysis and topic modeling. The app will have the following functionalities:

1. Data Ingestion: Users can upload CSV files containing customer reviews or feedback.
2. Sentiment Analysis: Utilize Amazon Bedrock's natural language processing capabilities to analyze the sentiment of each review (positive, neutral, negative).
3. Topic Modeling: Implement topic modeling to identify common themes within the feedback data.
4. Interactive Dashboard: Display the results of sentiment analysis and topic modeling in an interactive dashboard, allowing users to filter and explore the data based on different criteria such as date range, product category, etc.
5. Real-Time Updates: Ensure that the dashboard updates in real-time as new data is ingested.

The 'atoti-server-ai-amazon-bedrock' package will be used to facilitate the connection between Atoti, a powerful data analytics engine, and Amazon Bedrock, enabling seamless integration of AI services into the application. Your task is to write the necessary Python code to set up this integration, including setting up the server, configuring the data ingestion process, and implementing the AI models provided by Amazon Bedrock. Additionally, you should create an intuitive user interface using a library like Streamlit to make the dashboard accessible and user-friendly.

💬 Discussion Feed

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