🤖 AI Analysis

Final verdict: SUSPICIOUS

The package is flagged for its network activity, which could potentially be used for unexpected purposes such as data exfiltration or command and control communications. However, other risks like shell execution, obfuscation, and credential harvesting are minimal.

High network risk due to external API calls
Low risk in other categories, but concerns about maintainer activity

Per-check LLM notes

Network: The package makes network calls to an external API which could be unexpected and may indicate data exfiltration or C2 communication.
Shell: No shell execution patterns were detected.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: Low risk due to lack of suspicious indicators, but potential issues with maintainer history suggest low effort or inactivity.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

mes.""" try: with urllib.request.urlopen(f"{_OLLAMA_BASE}/api/tags", timeout=5) as resp:
odel}).encode() req = urllib.request.Request( f"{_OLLAMA_BASE}/api/pull", data
, ) try: with urllib.request.urlopen(req) as resp: last_status = ""

✓ 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 navid72m/adaptiveRAG appears legitimate

⚠ 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 adaptiverag

Create a mini-app called 'AgenticResearchTool' that leverages the 'adaptiverag' package to assist researchers in conducting literature reviews more efficiently. This app will use the agentic RAG (Retrieval-Augmented Generation) framework provided by 'adaptiverag', which integrates LangGraph and Ollama to enhance its capabilities.

The application should allow users to input a research query and receive a structured summary of relevant academic papers and articles. Additionally, it should provide recommendations for further reading based on the initial query and the retrieved information.

Step-by-Step Guide:
1. Set up the environment by installing necessary packages including 'adaptiverag'.
2. Integrate the 'adaptiverag' package into your app to enable the retrieval and summarization of relevant literature.
3. Design a user-friendly interface where users can enter their research queries.
4. Implement a feature that uses 'adaptiverag' to search through a database of academic papers and articles, returning a summary of the most relevant ones.
5. Develop a recommendation system that suggests additional readings based on the initial query and the retrieved documents.
6. Ensure that the app can handle different types of academic content formats and sources.
7. Test the app thoroughly to ensure accuracy and efficiency in retrieving and summarizing information.
8. Deploy the app so that it can be accessed via a web interface or as a desktop application.

Suggested Features:
- Integration with major academic databases (e.g., PubMed, IEEE Xplore).
- Customizable settings for users to refine their search criteria.
- A visual representation of the connections between different papers/articles.
- An option to export summaries and recommendations in various formats (PDF, Word).
- User authentication and personalized dashboards for saved searches and notes.