Zilliz x Galileo: The Power of Vector Embeddings

Unstructured data, which makes up 80% of global data, is becoming increasingly prevalent. Vector embeddings are numerical representations used to work with unstructured data such as text, images, audio, and videos. They can be extracted from trained machine-learning models and have high dimensionality to store complex data. Vector embeddings are the de facto way to work with unstructured data, allowing for comparisons between data points. When generating embedding vectors, factors like vector size, training data quality, and quantity should be considered. Vector embeddings can be used to debug training data by detecting errors through clustering, finding samples not present in the training data, identifying hallucinations, and fixing errors in retrieval augmented generation (RAG). Additionally, they can be indexed, stored, and queried using vector databases like Milvus or Zilliz Cloud. The power of vector embeddings is evident from their wide range of use cases, making them a valuable tool for working with unstructured data in machine learning applications.