The article debunks the claim that Retrieval‑Augmented Generation is obsolete, explains why huge context windows are impractical, and systematically presents ten RAG patterns—from basic Naïve RAG to advanced Graph and Multimodal RAG—detailing their trade‑offs, costs, and suitable use cases.
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 examines the key factors for selecting embedding models in Retrieval‑Augmented Generation, comparing dimensions, context windows, MTEB scores, pricing, and language support across major providers, and offers practical recommendations, cost estimates, and pitfalls to avoid.
This article explains Retrieval‑Augmented Generation (RAG), detailing its offline knowledge‑base construction and online retrieval‑enhanced generation workflow, comparing it with traditional and fine‑tuned models, and providing step‑by‑step LangChain implementations, advanced techniques, and practical use‑case demos.
This article explains the principle of vector embeddings, shows how they turn words, images, audio and other data into dense numeric vectors, compares them with one‑hot encoding, describes static and contextual models, training methods, similarity metrics, and a wide range of real‑world AI applications.
This guide explains how to organize a knowledge base, select embedding and rerank models, clean user queries, and construct a Dify DSL workflow that iteratively retrieves and merges information before handing it to a large language model for answer generation.
This article reviews the author’s hands‑on experience with Pinecone’s serverless vector database, various embedding and generation models such as all‑MiniLM‑L6‑v2, text‑embedding‑ada‑002, clip‑ViT‑B‑32, and GPT‑3.5‑turbo‑instruct, and demonstrates how they are applied to semantic search, RAG, recommendation, hybrid, and facial similarity tasks using Python code examples.
PetPS introduces a persistent‑memory‑based parameter server that redesigns indexing with the PetHash hash table and offloads parameter aggregation to NIC Gathering, achieving up to 1.7× higher throughput and significantly lower latency for industrial‑scale embedding models in recommendation, search, and advertising workloads.
