How to store BGE embeddings in PostgresSQL with pgvector?

This article discusses the integration of BGE Embedding model with PostgreSQL using pgvector extension for efficient storage, indexing, and querying of vector data. The BGE model is an open-source series from Beijing Academy of Artificial Intelligence that maps text to low-dimensional dense vectors. It can be utilized through Flag Embedding, Sentence-Transformers, LangChain, or Huggingface Transformers. The article also explains the concept of embeddings and their use cases in search engines, recommendation systems, text classification, document clustering, and sentiment analysis. Furthermore, it highlights why storing pre-computed embeddings in a database like pgvector is beneficial for faster retrieval and efficient similarity comparisons.