arvel-search

v0.6.0 safe
3.0
Low Risk

Scout-style full-text search for Arvel — Meilisearch, Elasticsearch, database, collection, and null drivers.

🤖 AI Analysis

Final verdict: SAFE

The package is deemed safe based on low risk scores across all categories and no suspicious activities detected.

  • Network interactions appear legitimate
  • No shell execution detected
  • Single package from author, no additional red flags
Per-check LLM notes
  • Network: The observed network patterns are likely legitimate for making asynchronous HTTP requests to specific services, suggesting the package interacts with remote servers.
  • Shell: No shell execution patterns were detected.
  • Metadata: The author has only one package, suggesting it may be a new or less active account, but no other red flags are present.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 9 test file(s) found

  • Test runner config found: conftest.py
  • 9 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://arvel.dev/packages/search/
  • Detailed PyPI description (3295 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 101 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 100 commits in mohamed-rekiba/arvel
  • Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

  • ey else {} async with httpx.AsyncClient(base_url=self._host, headers=headers, timeout=10.0) as clien
  • lf) -> None: client = httpx.AsyncClient(transport=_meili_transport(), base_url="http://meili")
  • skUid": 1}) client = httpx.AsyncClient(transport=httpx.MockTransport(handler), base_url="http://mei
  • lf) -> None: client = httpx.AsyncClient(transport=_es_transport({}), base_url="http://es") e
  • r, Any] = {} client = httpx.AsyncClient(transport=_es_transport(captured), base_url="http://es")
✓ 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

Repository mohamed-rekiba/arvel appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Arvel contributors" 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 arvel-search 
Create a book search engine mini-application using the 'arvel-search' Python package. This application will allow users to search for books based on their title, author, and description across a variety of sources, including a local database and external search engines like Meilisearch and Elasticsearch. The app should be designed to showcase the flexibility and power of 'arvel-search', allowing it to handle different types of data sources seamlessly.

### Features:
- **User Interface**: A simple web interface where users can input their search queries.
- **Search Functionality**: Users should be able to search for books by entering keywords related to the title, author, or description.
- **Data Sources**: The application should be capable of indexing and searching through a local SQLite database containing book information as well as external search engines like Meilisearch and Elasticsearch.
- **Driver Support**: Implement support for at least three different drivers provided by 'arvel-search': database, collection, and null.
- **Real-time Updates**: Ensure that the search results update in real-time as the user types their query.
- **Pagination**: Results should be paginated to improve usability.

### Steps to Build the Application:
1. **Setup Project Environment**:
   - Install necessary Python packages, including 'arvel-search', Flask for the web framework, and any required database connectors.
2. **Define Data Models**:
   - Create models for storing book information such as title, author, and description.
3. **Configure Search Engines**:
   - Set up Meilisearch and Elasticsearch instances if not already available.
4. **Integrate 'arvel-search'**:
   - Use 'arvel-search' to index book data from both the local database and the external search engines.
5. **Develop User Interface**:
   - Design a clean and intuitive interface using HTML/CSS/JavaScript for querying and displaying search results.
6. **Implement Real-time Search**:
   - Develop functionality that triggers searches as the user types, showing relevant results dynamically.
7. **Add Pagination**:
   - Implement pagination to display search results in manageable chunks.
8. **Testing**:
   - Thoroughly test the application to ensure all features work as expected, including search accuracy and performance.
9. **Deployment**:
   - Deploy the application to a cloud service provider like Heroku or AWS for public access.

This project will demonstrate the versatility of 'arvel-search' and provide a practical example of integrating multiple data sources into a single search interface.

💬 Discussion Feed

Leave a comment

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