embedding

What problem does it solve?

Generate high-dimensional vector representations for text to enable semantic search, clustering, and retrieval.

Core Features & Use Cases

• Batch and single-text embedding: Produce multiple embeddings at once or a single vector for a given input.
• Unified embedding service: Access a preloaded embedding model with a consistent interface.
• Use Case: Build a semantic search index for a document collection or feed embeddings into a similarity-based recommender.

Quick Start

Embed a sample text to generate its 2560-dimension vector using the unified embedding service.