Voyage AI Embeddings and Rerankers for Search and RAG

The article discusses Retrieval Augmented Generation (RAG), a technique that optimizes large language models by providing context from the query. It explains how embedding models convert unstructured data into vector embeddings, enabling computers to understand semantics. RAG is particularly useful in reducing hallucinations in generative AI models like ChatGPT. The article also introduces Voyage AI's domain-specific and general-purpose embedding models and rerankers that contribute significantly to search and RAG. Furthermore, it demonstrates how to integrate Zilliz Cloud Pipelines with Voyage AI for streamlined embedding generation and retrieval, using Cohere as the LLM to build a RAG application.