akv-cache

v1.2.0 suspicious
5.0
Medium Risk

Retrieval-preserving hierarchical KV cache compression for long-context LLM inference

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits some suspicious characteristics, primarily due to its anonymous author and low activity level, which raises concerns about its provenance and intentions.

  • Anonymous author
  • Low activity level
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external communication.
  • Shell: No shell execution patterns detected, indicating no immediate risk of unauthorized command execution.
  • Obfuscation: The observed patterns could indicate obfuscation but also legitimate usage of performance measurement and model evaluation in machine learning contexts.
  • Credentials: No clear evidence of credential harvesting detected.
  • Metadata: The package shows some red flags such as an anonymous author and low activity, but there are no clear signs of typosquatting or other malicious intent.

📦 Package Quality Overall: Medium (6.0/10)

✦ High Test Suite 9.0

Test suite present — 12 test file(s) found

  • Test runner config found: pyproject.toml
  • 12 test file(s) detected (e.g. test_adapters.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/Arvind679715/adaptive-kv-memory/tree/main
  • Detailed PyPI description (19456 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 252 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 67 commits in Arvind679715/adaptive-kv-memory
  • Single author but highly active (67 commits)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

  • time.perf_counter() model.eval() with torch.no_grad(): for text in sample_texts
  • ) self.model.eval() logger.info("Model loaded.") def load_dataset
  • lForCausalLM.from_config(cfg).eval() tok = AutoTokenizer.from_pretrained("hf-internal-t
✓ 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: ups.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ 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 akv-cache 
Create a Python-based mini-application named 'ContextCompress' that leverages the 'akv-cache' package to optimize long-context language model inference. This application should allow users to input large amounts of text and then compress it using the hierarchical KV cache compression method provided by 'akv-cache'. The goal is to reduce the context size while preserving retrieval accuracy for subsequent language model queries.

Key Features:
- User Interface: A simple command-line interface (CLI) for text input and output display.
- Compression Engine: Utilize 'akv-cache' to compress the input text based on its content and structure.
- Performance Metrics: Display the original and compressed text sizes, as well as the time taken for compression and decompression.
- Retention Check: After decompression, compare the original and decompressed texts to ensure minimal loss of information.

Steps to Implement:
1. Set up the development environment by installing necessary packages including 'akv-cache'.
2. Design the CLI to accept user inputs and process them through the 'akv-cache' engine.
3. Integrate 'akv-cache' into the application to handle the compression logic.
4. Implement functionality to measure and display performance metrics of the compression and decompression processes.
5. Develop a mechanism to verify the integrity of the decompressed text against the original input.
6. Test the application thoroughly with various types of text data to ensure robustness and efficiency.
7. Document the code and provide usage instructions for other developers.

💬 Discussion Feed

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