astrocyte-qdrant

v0.15.0 safe
3.0
Low Risk

Qdrant VectorStore adapter for Astrocyte

🤖 AI Analysis

Final verdict: SAFE

The package exhibits minimal risk indicators with no evidence of malicious intent or activity. It appears to be a straightforward adapter for integrating Qdrant into Astrocyte's VectorStore protocol.

  • No network calls detected.
  • No shell execution patterns.
  • No obfuscation or credential harvesting.
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution patterns detected, indicating no immediate signs of malicious activity.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows some low-effort indicators but lacks clear red flags.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 3 test file(s) found

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

Some documentation present

  • Brief PyPI description (523 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

  • 14 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

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

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-qdrant 
Develop a knowledge management system using the Python package 'astrocyte-qdrant' that allows users to store, search, and retrieve semantically similar documents efficiently. This mini-app will serve as a personal or team knowledge base, enabling users to input text documents and query them based on semantic similarity rather than exact keyword matches.

### Features:
1. **Document Storage**: Users should be able to upload multiple text documents into the system. These documents could range from articles, blog posts, to notes.
2. **Semantic Search**: Implement a feature where users can input a query and receive results based on semantic similarity rather than exact matches. For example, if a user searches for 'AI in healthcare', the system should return relevant documents even if they don't contain those exact words.
3. **User Interface**: Create a simple web-based interface for users to interact with the system easily. This includes uploading documents, querying the database, and viewing search results.
4. **Document Similarity Visualization**: Provide a feature that visualizes the similarity between different documents. This could be done through a graph or a matrix where closer nodes indicate higher similarity.
5. **Security and Privacy**: Ensure that all data stored in the system is secure and private. Implement basic security measures such as user authentication and encryption.

### How 'astrocyte-qdrant' is Utilized:
- **Vector Embedding**: Use 'astrocyte-qdrant' to convert text documents into vector embeddings, which capture the semantic meaning of the text. This step is crucial for enabling efficient semantic searching.
- **Indexing**: Store these vector embeddings in Qdrant, leveraging its capabilities for fast vector similarity searches.
- **Query Processing**: When a user inputs a query, convert it into a vector embedding and use Qdrant to find the most similar documents based on their embeddings.
- **Integration with Web Interface**: Integrate the functionalities provided by 'astrocyte-qdrant' with the web interface to allow seamless interaction between users and the backend processing.

💬 Discussion Feed

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