The content discusses the importance of embeddings in Retrieval Augmented Generation (RAG) and compares different types of embeddings with examples. It explains how static embeddings generate fixed vector representations for words, while contextual embeddings produce different vectors based on context. Examples illustrate how different embeddings handle queries and logs to retrieve relevant information. Limitations of each type of embedding are also discussed. The comparison includes BERT, RoBERTa, SBERT, and MPNet embeddings, highlighting their bidirectional and focused context capabilities. The content further discusses generative-based embeddings like GPT and their ability to generate responses based on broader context. Limitations of generative models are also mentioned. The article concludes with a comparison summary of various embeddings based on the MTEB retrieval score and latency. Resources for further reading on the topic are provided.
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Source link: https://medium.com/bright-ml/choosing-the-right-embedding-for-rag-in-generative-ai-applications-8cf5b36472e1?source=rss——artificial_intelligence-5
Selecting the Best Embedding for RAG in Generative AI #EmbeddingSelection
