OpenAI Text Embedding 3
embedding model
State-of-the-art embedding models from OpenAI
Supported languages
Text Embedding 3 is OpenAI's latest family of embedding models, designed to convert text into high-quality dense vectors. It includes large and small variants, offering the best balance between performance and cost for semantic search and RAG applications.
Concepts
dense-vectorssemantic-similaritycosine-distancedimensionalitybatch-embedding
Pros and Cons
Ventajas
- + Best performance on embedding benchmarks
- + Simple and easy-to-use API
- + Adjustable dimensions (256 to 3072)
- + Excellent for semantic search
- + Robust multilingual support
- + Competitive pricing
Desventajas
- - Requires OpenAI API connection
- - External service dependency
- - No local model available
- - Recurring usage costs
Casos de Uso
- Production RAG systems
- Enterprise semantic search
- Document clustering
- Duplicate detection
- Content recommendation