# kamauwashington/python-cosine-similarity architecture

> Architecture diagram of the GitHub repository kamauwashington/python-cosine-similarity, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-09-27). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/kamauwashington/python-cosine-similarity
- Repository: https://github.com/kamauwashington/python-cosine-similarity

## Overview

This small reference library compares two phrases by tokenizing them, building word-frequency vectors, and calculating cosine similarity. The tokenization helper and the remaining inline stages are all implemented in one module; no separate application or storage layer exists.

Main entry points: [`cosine_similarity.py`](https://github.com/kamauwashington/python-cosine-similarity/blob/main/cosine_similarity.py) (Cosine similarity)

## Diagram (Mermaid)

```mermaid
flowchart TD

node_user(("Caller"))
node_similarity["Cosine similarity"]
node_tokenizer["Word tokenizer"]
node_vectors["Frequency vectors"]
node_calculation["Cosine calculation"]

node_user -->|"provides phrases"| node_similarity
node_similarity -->|"tokenizes both"| node_tokenizer
node_similarity -->|"builds counts"| node_vectors
node_vectors -->|"supplies counts"| node_calculation
node_similarity -->|"computes similarity"| node_calculation
node_similarity -->|"returns score"| node_user

click node_similarity "https://github.com/kamauwashington/python-cosine-similarity/blob/main/cosine_similarity.py"
click node_tokenizer "https://github.com/kamauwashington/python-cosine-similarity/blob/main/cosine_similarity.py"
click node_vectors "https://github.com/kamauwashington/python-cosine-similarity/blob/main/cosine_similarity.py"
click node_calculation "https://github.com/kamauwashington/python-cosine-similarity/blob/main/cosine_similarity.py"

classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_user toneIndigo
class node_similarity,node_tokenizer,node_vectors,node_calculation toneTeal
```

## Components

### Other components

- **Caller**
- [Cosine similarity](https://github.com/kamauwashington/python-cosine-similarity/blob/main/cosine_similarity.py): `cosine_similarity.py`
- [Word tokenizer](https://github.com/kamauwashington/python-cosine-similarity/blob/main/cosine_similarity.py): `cosine_similarity.py`
- [Frequency vectors](https://github.com/kamauwashington/python-cosine-similarity/blob/main/cosine_similarity.py): `cosine_similarity.py`
- [Cosine calculation](https://github.com/kamauwashington/python-cosine-similarity/blob/main/cosine_similarity.py): `cosine_similarity.py`

## Connections

- Caller → Cosine similarity: provides phrases
- Cosine similarity → Word tokenizer: tokenizes both
- Cosine similarity → Frequency vectors: builds counts
- Frequency vectors → Cosine calculation: supplies counts
- Cosine similarity → Cosine calculation: computes similarity
- Cosine similarity → Caller: returns score