Features

This chapter covers the core features and capabilities of Vector Search Setup.

Features

• Cosine similarity — Manual implementation of dot product, magnitude, and cosine similarity
• LSH indexing — Locality-sensitive hashing for sub-linear approximate nearest neighbor search
• Exact search — Brute-force search for small indexes where perfect recall matters
• Text-to-vector — Hashed bag-of-words encoder converts text to fixed-dimension vectors
• Vocabulary builder — Automatic vocabulary extraction from your document corpus
• Index persistence — Save and load indexes to/from JSON files
• Performance benchmark — Compare exact vs. approximate search speed and recall
• CLI interface — Build indexes, query them, and run benchmarks from the terminal

Quick Start

# Run the interactive demo with built-in sample data
python src/vector_search_setup.py --demo

# Build an index from a JSONL file
python src/vector_search_setup.py --build-index data.jsonl --output my_index.json

# Query an existing index
python src/vector_search_setup.py --query "machine learning algorithms" --index my_index.json --top-k 5

# Run performance benchmark
python src/vector_search_setup.py --benchmark --dim 128 --num-vectors 5000

Project Structure

Follow this guide to get Vector Search Setup up and running in your environment.

Project Structure

vector-search-setup/
├── README.md
├── LICENSE
├── src/
│   └── vector_search_setup.py    # Core engine (~400 lines)
└── examples/
    ├── basic_usage.py             # Programmatic usage example
    └── sample_documents.jsonl     # Sample data for index building

CLI Reference