pynamer/memory-bank/techContext.md

Tech Context: PyNamer

Language: Python 3

Core Libraries:

• litellm: For interacting with various LLM APIs (OpenAI, Anthropic, etc.). Handles model routing, API key management, and standardized response format.
• PyYAML: For parsing the config.yaml configuration file.
• Pillow: For image manipulation (opening, resizing, saving to buffer).
• argparse: Standard library for parsing command-line arguments.
• base64: Standard library for encoding image data.
• io: Standard library for handling in-memory byte streams (used with Pillow).
• os, pathlib: Standard libraries for file system operations.
• logging: Standard library for application logging.

Dependencies:

• Listed in requirements.txt.
• Key dependencies: litellm, pyyaml, Pillow.

Setup & Execution:

1. Installation:

   pip install -r requirements.txt 
   # or potentially: pip install . (if setup.py is configured correctly)
   

2. Configuration:
   • Create or modify config.yaml.
   • Set LLM api_key in the config or via environment variable (e.g., OPENAI_API_KEY).
   • Adjust model, max_tokens, temperature, resize_max_dimension, resize_format, and prompts as needed.
3. Execution:

   python pynamer.py <image_path_1> [image_path_2 ...] [-c config.yaml] [-d] [-v]
   

   • <image_path>: Path to the image file(s). Handles paths with spaces.
   • -c: Specify a different config file path.
   • -d: Dry run (preview changes).
   • -v: Verbose logging.

Environment:

• Assumes a standard Python environment where dependencies can be installed via pip.
• Relies on network access to reach the configured LLM API endpoint.