Simple RAG for text summarization

How to run the scripts

1. clone the repo

git clone https://github.com/makarwu/RAGsum.git

2. install dependences

python3 -m venv env
source env/bin/activate
pip install -r requirements.txt

3. run scripts

sims.py: python sims.py "Meaning of life" --metric cosine --file "moby-dick.txt"

• try out youre own txt files. Just upload them to the base directory "./" of the repo
• added different similarity metrics (cosine, dot, euclidean, manhattan) to demonstrate the differences

Similarities

1. The dot product is used to find the angle between two vectors and to project one vector onto another, it provides a way to measure the alignment between to vectors.

a * b = a_1 * b_1 + a_n * b_n

2. Cosine similarity divides the angle of the vectors by the length (magnitude) of the vectors

cosine_similarity(A, B) = (A dot B) / (|A| * |B|)

3. Euclidean Distance measures the straight-line distance between two points.

d(P,Q) = sqrt((x2 - x1)^2 + (y2 - y1)^2 + ... + (zn - z1)^2)

4. Manhattan Distance demonstrates the distance between two points in a grid-based system.

d = |x2 - x1| + |y2 - y1| + ... + |wn - wn|