Understanding embedding models: make an informed choice for your RAG

Selecting a suitable embedding model for a Retrieval-Augmented Generation (RAG) application involves understanding the differences between Bi-Encoders and Cross-Encoders, both of which are benchmarked using the Massive Text Embedding Benchmark (MTEB) leaderboard. Bi-Encoders, often used for initial document embeddings, provide vector representations of text by processing documents and queries separately, which facilitates efficient similarity searches via pre-computed embeddings. In contrast, Cross-Encoders assess similarity by examining text pairs simultaneously and are more effective in reranking retrieved results due to their ability to capture nuanced relationships, albeit with higher computational costs. The MTEB leaderboard helps in evaluating embedding models by considering metrics like NDCG@10, which assesses retrieval performance, and allows users to refine their model choices based on language, domain, and dataset-appropriate metrics. To optimize RAG performance, strategies such as adjusting chunk sizes, incorporating hybrid search, leveraging metadata, and fine-tuning models on personalized datasets can be employed, thereby enhancing the retrieval accuracy and efficiency of the RAG system.