astrocyte-elasticsearch

v0.15.0 safe
4.0
Medium Risk

Elasticsearch DocumentStore adapter for Astrocyte

🤖 AI Analysis

Final verdict: SAFE

The package appears to serve a legitimate purpose as an Elasticsearch adapter with minimal risk signals. It has no detected malicious activities or high-risk behaviors.

  • Low network, shell, obfuscation, and credential risks
  • Metadata suggests new or less maintained status but no malicious indicators
Per-check LLM notes
  • Network: The use of urllib to make network calls might be legitimate depending on the package's functionality, but requires verification to ensure it is not used for unintended purposes.
  • Shell: No shell execution patterns detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets and credentials.
  • Metadata: The package shows signs of low maintainer effort and potential newness, but lacks clear malicious indicators.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 2 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: pyproject.toml
  • 2 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 5.0

Some documentation present

  • Brief PyPI description (429 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 10 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • mport urllib.request urllib.request.urlopen(ES_URL, timeout=2.0).read() return True
✓ 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

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with astrocyte-elasticsearch 
Create a Python-based document search engine that leverages the 'astrocyte-elasticsearch' package to interface with an Elasticsearch cluster. This application will serve as a robust tool for indexing and searching through large collections of documents, making it ideal for academic research, legal documentation, or any scenario where comprehensive document retrieval is necessary. Here’s a detailed breakdown of the steps and features you should include in your project:

1. **Setup**: Begin by installing the required packages including 'astrocyte-elasticsearch', 'elasticsearch', and any other dependencies needed.
2. **Configuration**: Configure the application to connect to an existing Elasticsearch cluster or set up a local one for testing purposes. Ensure that the configuration allows for secure and efficient data handling.
3. **Document Indexing**: Implement functionality to index documents into the Elasticsearch cluster. These documents could range from simple text files to complex PDFs or Word documents. Utilize 'astrocyte-elasticsearch' to efficiently map these documents into searchable formats within the Elasticsearch DocumentStore.
4. **Search Functionality**: Develop a user-friendly search interface that allows users to query the indexed documents based on various criteria such as full-text search, metadata filtering, etc. Use the capabilities provided by 'astrocyte-elasticsearch' to enhance the relevance and speed of search results.
5. **Advanced Features**:
   - **Faceted Search**: Allow users to narrow down their searches using facets like date ranges, document types, authors, etc.
   - **Highlighting**: Implement highlighting to visually emphasize the search terms within the retrieved documents.
   - **Pagination**: For large result sets, implement pagination to manage the display of results efficiently.
6. **Security & Performance**: Ensure that the application adheres to best practices for security and performance, including secure connections to Elasticsearch, efficient use of resources, and optimized query performance.
7. **Documentation**: Provide thorough documentation explaining how to install, configure, and use the application, including examples of common queries and operations.

By following these steps, you will create a powerful and flexible document search engine that showcases the capabilities of the 'astrocyte-elasticsearch' package.

💬 Discussion Feed

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