LLaMa Homework Agent

Local LLaMa RAG to help with homework through locally-saved textbooks. The scripts extract text from a PDF file and generates a summary using AI models (GPT or LLaMA).

Installation

Ensure you have the required dependencies installed:

pip install langchain_community langchain_openai pytesseract pdf2image tqdm

Additionally, install Tesseract OCR and Poppler:

• Linux (Ubuntu/Debian):

  sudo apt install tesseract-ocr poppler-utils

• MacOS:

  brew install tesseract poppler

• Windows:
  1. Download and install Tesseract from Tesseract GitHub.
  2. Download and install Poppler from Poppler for Windows.
  3. Add both to your system PATH.

Usage

Run the script using the following command:

python script.py --pdf_file path/to/input.pdf --output_file path/to/output.txt

Available Command-line Arguments:

Argument	Description	Default Value
--summary_format_files	Path to the summary format file	prompts/summary_format.txt
--prompt_file	Path to the prompt template file	prompts/story_summary.txt
--use_gpt	Use GPT-based model (True) or LLaMA (False)	True
--pdf_file	Path to the input PDF file (Required)	No Default (Required)
--output_file	Path to save the output text summary	outputs/summary.txt
--chunk_size	Number of pages to process at once	5

Example Commands:

1. Summarize using GPT (default):

   python script.py --pdf_file myfile.pdf --output_file mysummary.txt

2. Summarize using LLaMA:

   python script.py --pdf_file myfile.pdf --output_file mysummary.txt --use_gpt False

3. Process PDF in chunks of 10 pages:

   python script.py --pdf_file myfile.pdf --chunk_size 10

Troubleshooting

• Ensure Tesseract and Poppler are installed and properly configured in your environment.
• For large PDFs, increase system memory or reduce --chunk_size.