AI Embeddings Models Tutorial: OpenAI, Cohere, Bge

TL;DREmbedding models (OpenAI, Cohere, BGE) turn text into vectors for search and RAG.

Dedicated embedding models are optimized to map text to vectors where semantic similarity = closeness — the retrieval engine behind search, RAG, clustering, and recommendations. Options range from API services (OpenAI, Cohere) to strong open models (BGE, E5). Key choices: dimension (storage/speed), domain fit, and matching the same model for indexing and querying. Good embeddings are half of good retrieval.

Key points

Text → similarity-preserving vectors
Engine behind search, RAG, clustering
API (OpenAI/Cohere) or open (BGE/E5)
Use the same model to index and query

Common mistakes

Mixing embedding models for index vs query
Ignoring domain fit of the model
Over-paying for dimensions you don’t need

Try it: Explain why index and query must use the same embedding model.

Example code

<!doctype html><html><head><meta charset="utf-8"></head>
<body style="background:#06040d;color:#e6e0ff;font-family:monospace;padding:20px"><pre>docs → embeddings (model X) → vector DB
query → embedding (model X) → nearest docs
mismatched models → broken similarity</pre></body></html>

Open the interactive lesson →

← Video Generation · Sora · Veo Vector Dbs · Pinecone · Weaviate · Chroma →