Vector embedding demos

A vector embedding encodes an input as a list of floating point numbers.

"dog" → [0.017198, -0.007493, -0.057982, 0.054051, -0.028336, 0.019245,…]

Different models output different embeddings, with varying lengths.

Model	Encodes	Vector length
word2vec	words	300
Sbert (Sentence-Transformers)	text (up to ~400 words)	768
OpenAI text-embedding-ada-002	text (up to 8191 tokens)	1536
OpenAI text-embedding-3-small	text (up to 8191 tokens)	256-1536
OpenAI text-embedding-3-large	text (up to 8191 tokens)	256-3072
Azure AI Vision	image or text	1024

Vector embeddings are commonly used for similarity search, fraud detection, recommendation systems, and RAG (Retrieval-Augmented Generation).

This repository contains a visual exploration of vectors, using several embedding models.

Before running the notebooks, install the requirements:

pip install -r requirements.txt

Then explore these notebooks:

• Generate new OpenAI text embeddings
• Compare OpenAI and Word2Vec embeddings
• Vector similarity
• Vector search
• Generate multimodal vectors for dataset
• Explore multimodal vectors
• Vector distance metrics
• Vector quantization
• Vector dimension reduction (MRL)

These notebooks are also provided, but aren't necessary unless you're generating new embeddings data.

• Generate OpenAI embeddings for datasets
• Generate Word2Vec embeddings for datasets

Resources

Each notebook has resources at the bottom to dig further into that topic. Here are some additional general resources:

• Poster: Visual exploration of vectors (PyCon 2025)
• Livestream Recording: Vector Embeddings from Python + AI Series
• Vector compression techniques