This article explains why embedding models are the foundation of RAG pipelines, outlines concrete evaluation metrics such as MTEB v2 scores, latency, throughput and cost, compares a range of commercial and open‑source models, and discusses emerging trends like multimodal and long‑context embeddings.
This article explains the role of embedding models in Retrieval‑Augmented Generation, compares the most popular 2024‑2025 open‑source embeddings and rerankers, offers concrete selection rules, shows how to read the MTEB leaderboard, and provides a structured answer framework for interviewers.
Conan‑Embedding‑V2, a newly trained 1.4 B‑parameter LLM with a custom tokenizer, 32 k token context, SoftMask, cross‑lingual retrieval data and dynamic hard‑negative mining, delivers state‑of‑the‑art multilingual embeddings that surpass larger models on both English and Chinese MTEB benchmarks while remaining compact and fast.
This guide explains how to leverage the Massive Text Embedding Benchmark (MTEB) to identify high‑performing embedding models for Retrieval‑Augmented Generation (RAG) and outlines key factors such as model size, dimension, language support, resource requirements, inference speed, domain suitability, long‑text handling, scalability, and cost.
