🧠 Vector Algebra RAG Application (Local ML Utility) A locally executable Python-based application designed to implement and experiment with core vector algebra operations from scratch—useful in machine learning pipelines and retrieval-augmented generation (RAG) architectures, where embedding vectors and similarity math play a vital role. This project builds reusable, lightweight vector functionality without relying on external libraries, ideal for educational, research, or prototype-level systems where full ML libraries may be overkill or unavailable.

🚀 Key Features 🧮 Custom Vector Class: Create and manipulate mathematical vectors natively in Python 🧠 RAG Utility Ready: Supports core operations like dot product and norm—essential for vector similarity and scoring ⚙️ No External Dependencies: Fully native Python; easy to integrate, run, and extend 💡 Designed for Learning and Prototyping: Great for ML students, interview prep, and low-resource scenarios

🛠️ How to Use

Clone the repo

git clone https://github.com/Ruchir-Huchgol/machine_leaarning_projects.git cd machine_leaarning_projects

Run a test script or open interactive shell

python3

from vector2 import Vector v1 = Vector([1, 2, 3]) v2 = Vector([4, 5, 6]) print(v1 + v2) # Output: Vector([5, 7, 9]) To run with unit tests (if available in vector2.py): pip install pytest pytest

🔍 Comparative Design Study: vector.py vs vector2.py

Aspect	vector.py	vector2.py
Design Style	Mutable objects, in-place ops	Immutable, functional design
Operator Overloading	Basic: +, -, *	Extended: @ (dot), norm(), comparison ops
Error Handling	assert-based checks	Proper exceptions and validation
Performance	Manual loops	Pythonic with zip() and comprehensions
Testing	Minimal	Includes pytest-based tests
Documentation	Basic docstrings	Rich docstrings and usage examples

📌 Takeaway: The comparison highlights design trade-offs around performance, reliability, and code quality. These implementations demonstrate engineering depth, making this project a solid talking point for interviews and placements.