annie-sdk

v0.4.6 suspicious
5.0
Medium Risk

Python SDK for querying databases using natural language via the Annie API

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has moderate risk due to its low maintainer activity and poor metadata quality, which may indicate potential issues. However, it does not exhibit signs of immediate malicious behavior.

  • Low maintainer activity
  • Poor metadata quality
Per-check LLM notes
  • Network: The package makes network calls to an API endpoint, which could be legitimate if the SDK is designed to interact with a service.
  • Shell: No shell execution patterns detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, indicating low risk of unauthorized access.
  • Metadata: The package shows low maintainer activity and poor metadata quality, which could indicate a lower level of trustworthiness.

📦 Package Quality Overall: Low (3.6/10)

✦ High Test Suite 9.0

Test suite present — 6 test file(s) found

  • Test runner config found: conftest.py
  • 6 test file(s) detected (e.g. conftest.py)
○ Low Documentation 1.0

No documentation detected

  • No documentation URL, doc files, or meaningful description found
○ 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

  • 68 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 9.0

Found 6 network call pattern(s)

  • client self._client = httpx.Client( base_url=self._api_url, headers={
  • = [] self._client = httpx.Client( base_url=self._api_url, headers={
  • without constraints...") r = httpx.post( f"{API_URL}/v2/sdk/query", headers=headers, jso
  • tenant_id constraint...") r = httpx.post( f"{API_URL}/v2/sdk/query", headers=headers, jso
  • with two constraints...") r = httpx.post( f"{API_URL}/v2/sdk/query", headers=headers, jso
  • g with IN constraint...") r = httpx.post( f"{API_URL}/v2/sdk/query", headers=headers, jso
✓ 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 annie-sdk 
Create a mini-application named 'QueryBot' that leverages the 'annie-sdk' package to enable users to query databases using natural language inputs. This application will serve as a bridge between users and complex database structures, simplifying the process of data retrieval through conversational queries.

Step 1: Set up the Project
- Initialize a new Python project.
- Install the 'annie-sdk' package.
- Configure your project to connect to a sample database (e.g., SQLite).

Step 2: Design the User Interface
- Develop a simple command-line interface (CLI) where users can input their queries.
- Implement a feature to display query results in a user-friendly format.

Step 3: Integrate 'annie-sdk'
- Use 'annie-sdk' to parse natural language queries into structured SQL commands.
- Execute these SQL commands against the configured database.
- Retrieve and format the results for display.

Step 4: Enhance Functionality
- Add support for multiple database types (e.g., MySQL, PostgreSQL).
- Implement error handling for invalid queries or database connection issues.
- Include a feature to suggest possible corrections for misinterpreted queries.

Step 5: Testing and Validation
- Test the application with various types of queries to ensure accuracy.
- Validate the performance and reliability of the application under different conditions.

Features:
- Natural Language Querying: Allow users to enter queries in plain English.
- Multi-Database Support: Enable connections to different types of databases.
- Error Handling: Provide meaningful feedback for errors and misinterpretations.
- User-Friendly Output: Display results in an easy-to-understand format.

Utilizing 'annie-sdk':
- The 'annie-sdk' package will be the core component responsible for converting natural language inputs into executable SQL queries. It will handle the parsing logic, ensuring that the application can interpret a wide range of user queries accurately and efficiently.

💬 Discussion Feed

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