azure-search-documents

v12.0.0 safe
3.0
Low Risk

Microsoft Corporation Azure Search Documents Client Library for Python

🤖 AI Analysis

Final verdict: SAFE

The package appears to be legitimate with minimal risks. While there are some concerns about incomplete author metadata and potential obfuscation, these do not strongly indicate malicious intent.

  • Low network and shell execution risks.
  • No evidence of credential harvesting.
  • Potential for legitimate use of Base64 encoding.
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires them for functionality.
  • Shell: No shell execution patterns detected, indicating no direct system command execution.
  • Obfuscation: The observed patterns likely represent legitimate Base64 decoding operations used for handling encoded strings, possibly for URLs or other serialized data.
  • Credentials: No suspicious patterns indicating credential harvesting or secret theft were identified.
  • Metadata: The author's information is incomplete and the account seems new or inactive, raising some concerns but not strong indicators of malice.

📦 Package Quality Overall: Medium (5.0/10)

○ Low Test Suite 1.0

No test suite detected

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

Some documentation present

  • Detailed PyPI description (82365 chars)
○ 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

  • 481 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 35 unique contributor(s) across 100 commits in Azure/azure-sdk-for-python
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

  • unpacked_token = json.loads(base64.b64decode(token)) next_link = unpacked_token["nextLink"] next_
  • return attr return bytes(base64.b64decode(attr)) def _deserialize_bytes_base64(attr): if isinsta
  • ce("_", "/") return bytes(base64.b64decode(encoded)) def _deserialize_duration(attr): if isinstan
✓ 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: microsoft.com> license-expression: mit

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Azure/azure-sdk-for-python 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 azure-search-documents 
Create a document search utility using the Azure Cognitive Search service via the 'azure-search-documents' Python package. This utility will allow users to index their documents and perform advanced searches on them. Here’s a step-by-step guide to building this mini-app:

1. **Setup and Configuration**: Begin by setting up your Azure Cognitive Search service instance if you haven't already. Obtain the necessary API keys and endpoint URL. Install the 'azure-search-documents' package in your Python environment.

2. **Document Indexing**: Develop functionality to upload and index various types of documents (PDFs, Word Docs, etc.) into your Azure Cognitive Search service. Ensure that the indexing process extracts meaningful metadata and content from each document.

3. **Search Interface**: Implement a simple command-line interface (CLI) or a basic web interface where users can input search queries. Utilize the 'azure-search-documents' package to interact with the Azure Cognitive Search service for executing these queries.

4. **Advanced Search Features**: Enhance the search functionality by adding features like fuzzy search, semantic search, and faceted navigation. Users should be able to refine their searches based on specific criteria such as date ranges or document type.

5. **Result Display**: Design a user-friendly display format for the search results. Include relevant snippets from the documents, metadata, and links to view/download the original files.

6. **Security and Permissions**: If applicable, implement role-based access control to restrict certain users from accessing sensitive documents. Use Azure AD authentication to secure your application.

7. **Testing and Documentation**: Thoroughly test your application to ensure it works as expected under various scenarios. Provide comprehensive documentation detailing how to use the app, including setup instructions and troubleshooting tips.

By following these steps, you'll create a powerful yet easy-to-use document management tool leveraging the capabilities of Azure Cognitive Search.

💬 Discussion Feed

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