BAAI/bge-large-en-v1.5

Support two models：
BAAI/bge-large-en-v1.5
BAAI/bge-large-zh-v1.5

Text embeddings are used to measure the relevance between text strings. Embeddings are typically used for:

• Search (where results are ranked based on relevance to the query string)
• Clustering (where text strings are grouped by similarity)
• Recommendations (suggesting items related to relevant text strings)
• Anomaly Detection(identifying outliers that are different or less relevant)
• Diversity Measurement (analyzing the distribution of similarities)
• Classification (categorizing text strings by the most similar label)

An embedding is a list of floating-point numbers (a vector). The distance between two vectors measures their relevance. A small distance indicates high relevance, while a large distance indicates low relevance.

**Price：0.02 PTC / 1M tokens