ai-parrot-embeddings

v0.1.7 safe
3.0
Low Risk

Concrete embedding, vector-store, and reranker backends for AI-Parrot

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators with no network calls, shell executions, or credential harvesting attempts. The obfuscation is likely for performance reasons rather than malicious intent.

  • No network calls
  • No shell execution
  • Obfuscation for performance optimization
Per-check LLM notes
  • Network: No network calls detected, which is normal for a package focused on embeddings.
  • Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
  • Obfuscation: The obfuscation patterns appear to be related to performance optimization and do not indicate malicious intent.
  • Credentials: No credential harvesting patterns detected in the provided code snippet.
  • Metadata: The author's information is lacking, and they may be new or inactive, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 7 test file(s) found

  • 7 test file(s) detected (e.g. test_cross_dist_matryoshka.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (4428 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
  • 161 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 100 commits in phenobarbital/ai-parrot
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • n optimizations model.eval() if str(device) == "cuda": model.half()
  • ) model.eval() # Apply precision if self._precis
✓ 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: phenobarbital.info>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository phenobarbital/ai-parrot 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 ai-parrot-embeddings 
Create a mini-application named 'QueryEnhancer' using the Python package 'ai-parrot-embeddings'. This application aims to enhance user queries by providing contextually relevant suggestions and improving the precision of search results. Here's a detailed breakdown of what your application should do:

1. **Setup and Configuration**: Begin by setting up a Python virtual environment and installing necessary packages including 'ai-parrot-embeddings'. Ensure you configure any required API keys or credentials needed for accessing external services.

2. **User Query Input**: Design a simple UI or command-line interface where users can input their queries. These queries could range from general information requests to more specific questions about topics like technology, science, or entertainment.

3. **Query Embedding**: Utilize 'ai-parrot-embeddings' to convert user queries into embeddings. This process involves mapping the textual query into a numerical format that captures its semantic meaning.

4. **Contextual Suggestion Generation**: Based on the embedded query, generate contextual suggestions that help refine the original query. For instance, if a user asks about 'AI Parrots', the system might suggest looking into 'natural language processing', 'machine learning', or 'artificial intelligence applications'.

5. **Search Result Enhancement**: Use the generated embeddings to fetch and rank relevant documents or web pages. Enhance the search results by highlighting key phrases within the content that closely match the user’s query and provided suggestions.

6. **Feedback Loop**: Implement a feedback mechanism where users can rate the relevance of the suggested enhancements and search results. This feedback will be used to improve future suggestions and rankings.

7. **Integration with External APIs**: Optionally, integrate with external APIs (such as Google Search API or Wikipedia API) to provide richer, more diverse content in response to user queries.

8. **Testing and Deployment**: Thoroughly test the application to ensure it handles various types of queries effectively. Once satisfied, deploy the application either as a standalone desktop application or a web-based service accessible via a browser.

Incorporate 'ai-parrot-embeddings' throughout the application development process, leveraging its capabilities in embedding generation, vector storage, and reranking to deliver enhanced query experiences.