embedding

Generate text embeddings by converting input text into fixed 2560-dimension vectors. This creates the semantic representations needed to power similarity search, retrieval, and clustering applications.

Yes, you can process batch texts into vectors at once. The embedding service handles both single-text and multiple-text inputs, producing individual or multiple high-dimension vectors simultaneously for bulk indexing.

Yes, a preloaded embedding model is required to use this unified embedding service. The environment must have the model preloaded to provide the consistent interface that generates 2560-dimension vectors.

The best way to build a similarity-based recommender is to feed the generated 2560-dimension vector representations into your recommender logic. Converting text to fixed-dimension vectors enables real-time similarity matching for recommendations.

Text similarity clustering works by grouping items based on their generated vector embeddings. Converting text into fixed-dimension vectors allows clustering algorithms to measure semantic distance and group similar texts together.

The unified embedding service operates with a fixed 2560 dimensions for all vectors. While suitable for real-time semantic search and retrieval, applications requiring different vector dimensions or custom model architectures will face this constraint.