aicaller

v1.1.11 safe
3.0
Low Risk

Package for using API models. It is designed mainly for experimenting with various models. It allows to easily process Hugging Face datasets, or JSONL/CSV datasets, and send requests to Ollama and OpenAI compatibles APIs with just using simple configuration files.

🤖 AI Analysis

Final verdict: SAFE

The package appears to be primarily focused on model experimentation and does not exhibit significant risks. While there are some minor concerns regarding network calls and obfuscation, these do not strongly indicate malicious intent.

  • Low shell and credential risks
  • Moderate network and obfuscation risks
  • No clear signs of supply-chain attack
Per-check LLM notes
  • Network: The observed network call pattern suggests the package may be fetching images, which is not inherently suspicious but should be reviewed based on the package's intended functionality.
  • Shell: No shell execution patterns detected, indicating low risk of direct command execution.
  • Obfuscation: The observed pattern is likely for decoding and displaying images rather than malicious obfuscation.
  • Credentials: No suspicious patterns related to credential harvesting were detected.
  • Metadata: The package shows low activity and poor metadata quality, but there are no clear signs of malicious intent.

📦 Package Quality Overall: Medium (5.4/10)

✦ High Test Suite 9.0

Test suite present — 5 test file(s) found

  • 5 test file(s) detected (e.g. test_few_shot.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (4000 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

  • 100 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 24 commits in mdocekal/ai_caller
  • Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • r = requests.get(image, stream=True) if r.sta
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • image = Image.open(BytesIO(base64.b64decode(image))) token_cnt += calcu
✓ 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 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 "Martin Dočekal" 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 aicaller 
Create a conversational AI chatbot application that leverages the 'aicaller' Python package to interact with various AI models. This chatbot will allow users to input questions or statements and receive responses generated by different AI models available through APIs like OpenAI and Ollama. The application should support easy switching between different models by simply changing the configuration file, making it ideal for comparing the performance of various AI models in generating human-like responses.

### Features:
1. **User Interface**: Develop a simple command-line interface (CLI) for user interaction. Users should be able to type in their queries and receive model-generated responses.
2. **Model Configuration**: Implement functionality to load model configurations from a YAML file. This file should specify which API to use (e.g., OpenAI, Ollama), the model name, API key, and any other necessary parameters.
3. **Dataset Support**: Allow the chatbot to optionally load and preprocess datasets (in JSONL or CSV format) to train or fine-tune the AI models on specific tasks before responding to user inputs.
4. **Response Generation**: Use the 'aicaller' package to send requests to the specified AI models and process their responses. Ensure the application can handle different types of responses and errors gracefully.
5. **Logging and Analytics**: Include basic logging capabilities to record interactions and response times. This could help in analyzing the performance of different models over time.
6. **Switchable Models**: Make the model switchable via configuration changes without needing to restart the application. This feature will enable quick testing and comparison of multiple AI models.

### Steps to Build the Application:
1. **Setup Environment**: Install Python and the required packages including 'aicaller'.
2. **Design the CLI**: Create a basic command-line interface for user interaction.
3. **Configuration File**: Design a sample YAML configuration file to demonstrate how to specify model details.
4. **Implement Dataset Loading**: Integrate functionality to load and preprocess datasets as needed by the AI models.
5. **Integrate 'aicaller'**: Utilize the 'aicaller' package to make API calls and process responses. Handle different response formats and potential errors.
6. **Testing and Validation**: Test the application with various datasets and configurations to ensure it works as expected across different scenarios.
7. **Enhancements**: Consider adding enhancements such as more sophisticated logging, user feedback mechanisms, or additional model types.