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Embeddings: Techniques and Best Practices

Embeddings: Techniques and Best Practices

Embeddings: Techniques and Best Practices

Embeddings convert text into dense vector representations that capture semantic meaning. They are the foundation of semantic search, clustering, recommendation systems, and retrieval-augmented generation.

Embedding Models

Different embedding models excel at different tasks. OpenAI text-embedding-ada-002 (1536 dimensions) is a strong general-purpose model. text-embedding-3-small (512-1536) offers better performance at lower cost. Sentence-transformers (all-MiniLM-L6-v2, 384 dimensions) run locally.

Multilingual embeddings support cross-lingual retrieval. intfloat/multilingual-e5-large works across 100+ languages. Cohere embed-multilingual supports semantic search in multiple languages.…