RAG

Retrieval-Augmented Generation (RAG) is a robust technique that enhances language models by incorporating external information retrieval mechanisms. While standard RAG implementations improve response relevance, they often struggle in complex retrieval scenarios. This text explores...

Suppose an AI assistant fails to reply an issue about current events or provides outdated information in a critical situation. This scenario, while increasingly rare, reflects the importance of keeping Large Language Models (LLMs)...

Generative diffusion models like Stable Diffusion, Flux, and video models corresponding to Hunyuan depend on knowledge acquired during a single, resource-intensive training session using a hard and fast dataset. Any concepts introduced after this...

Using Qwen2.5–7B-Instruct powered code agents to create an area, open source, multi-agentic RAG systemLet’s dive into the small print of the workings of the agents involved within the architecture.Manager agentThat is the top-level agent,...

Essential metrics and methods to reinforce performance across retrieval, generation, and end-to-end pipelinesIntroductionWhen we expect of a few of the most typical applications of Generative AI, Retrieval-Augmented Generation (RAG) has indisputably surfaced to turn...

Imports & Data LoadingWe start by importing a couple of handy libraries and modules.import jsonfrom transformers import CLIPProcessor, CLIPTextModelWithProjectionfrom torch import load, matmul, argsortfrom torch.nn.functional import softmaxNext, we’ll import text and image chunks from...

What's RAG (Retrieval-Augmented Generation)?Retrieval-Augmented Generation (RAG) is a method that mixes the strengths of enormous language models (LLMs) with external data retrieval to enhance the standard and relevance of generated responses. Traditional LLMs use...

Going beyond the present Search Augmented Generation (RAG), which conducts searches based on a single knowledge source, predictions have emerged that so-called 'RAG agents', which extract information from multiple knowledge sources using various tools,...