From Word2Vec to LLM2Vec: How to Choose the Right Embedding Model for RAG

What this post added

This post extends the understanding of the AI Data Storage Engine by detailing the critical role of embedding models in RAG systems, which are often used in conjunction with vector databases like Milvus. It provides a comprehensive guide on evaluating and selecting embedding models based on factors such as context window, tokenization, dimensionality, vocabulary size, training data, cost, MTEB scores, and domain specificity. This information is crucial for users to effectively leverage Milvus for RAG applications by ensuring relevant data is retrieved based on high-quality embeddings.