Features

This chapter covers the core features and capabilities of Embedding Generator.

Features

• Multiple embedding methods — Hash-trick, Bag-of-Words, and TF-IDF
• Tokenizer — Configurable tokenization with stop-word removal
• Similarity search — Cosine similarity ranking over a corpus
• LRU caching — Automatic embedding cache with configurable size
• Corpus builder — Build vocabulary and IDF from your documents
• CLI interface — Embed text, load corpora, and search from terminal
• Zero dependencies — Python stdlib only

Quick Start

Follow this guide to get Embedding Generator up and running in your environment.

Quick Start

# Embed a single text
python src/embedding_generator.py --text "machine learning is great"

# Load a corpus and search
python src/embedding_generator.py --file examples/sample_corpus.txt --query "neural networks"

# Interactive mode
python src/embedding_generator.py

# Use TF-IDF method
python src/embedding_generator.py --method tfidf --file examples/sample_corpus.txt --query "data science"