Machine Learning

A small Python workspace for machine learning and document-processing experiments. The project includes notebooks for loading documents, sample text/PDF data, and dependencies for LangChain, ChromaDB, FAISS, and sentence transformers.

Project Structure

.
+-- data/
|   +-- pdf/          # Sample PDF files
|   +-- text_files/   # Sample text documents
+-- notebook/         # Jupyter notebooks and local ChromaDB data
+-- src/              # Python package source
+-- main.py           # Basic Python entry point
+-- pyproject.toml    # Project metadata and dependencies
+-- requirements.txt  # Dependency list

Setup

Create and activate a virtual environment:

python -m venv .venv
.\.venv\Scripts\Activate.ps1

Install dependencies:

pip install -r requirements.txt

Or, if you use uv:

uv sync

Usage

Run the basic entry point:

python main.py

Open the notebooks in Jupyter or VS Code:

jupyter notebook

The main notebook currently in use is:

notebook/pdf_loader.ipynb

Notes

• Keep API keys and local secrets in .env.
• Generated vector database files are stored under notebook/chroma_db/.
• Sample documents live under data/.