Many practitioners have found that although RAG can quickly build a demo in a short time, it faces numerous challenges in actual production environments. This article analyzes the core issue of RAG’s industrial implementation from the perspective of entrepreneurs in the AI large model field—problem grading—and discusses the challenges and solutions of four types of … Read more
Reprinted from WeChat Official Account | Blue’s Little Firefly In this article, I would like to accurately introduce the application areas of Knowledge Graphs (KG) in the RAG pipeline. We will explore the different types of questions that arise in the RAG pipeline and how to address these issues by applying knowledge graphs at various … Read more
In today’s business landscape, corporate data exhibits high diversity and complexity. Emails, invoices, resumes, support tickets, log messages, and tabular data all contain intricate conceptual relationships and contextual information. However, traditional single-vector embedding models struggle to capture and understand this complex multidimensional data structure, posing significant challenges for data retrieval and mining. The Current State … Read more
Overview The ContextChatEngine class is a contextual chat engine designed to provide a smooth chat experience by retrieving contextual information from the chat and using a language model (LLM) to generate responses based on system prompts. It is a simple chat mode built on top of a data retriever. For each chat interaction: First, retrieve … Read more
The full name of RAG is Retrieval Augmented Generation, which means “retrieval enhanced generation”. LLMs are trained on a vast amount of data, but this training data does not include your data. RAG solves this problem by adding your data to the data that the LLM already has access to. In RAG, your data is … Read more
https://arxiv.org/pdf/2501.09136 Overview of Retrieval-Augmented Generation (RAG) Retrieval-Augmented Generation (RAG) represents a significant advancement in the field of artificial intelligence by combining the generative capabilities of Large Language Models (LLMs) with real-time data retrieval. While LLMs excel in natural language processing, their reliance on static pre-trained data often results in outdated or incomplete responses. RAG achieves … Read more
In the field of artificial intelligence, large language models (LLMs) have achieved significant accomplishments. However, due to their reliance on static training data, they often struggle to respond effectively to dynamic real-time queries. Retrieval-Augmented Generation (RAG) technology has emerged, bringing new hope to address this issue. Agentic RAG further breaks through the limitations of traditional … Read more
Click 👇🏻 to follow, article from “ With the expanding application of large language models (LLMs), Retrieval-Augmented Generation (RAG) has become a mature technology. The popularity of products like txt2sql and ChatBI highlights the increasing importance of query construction techniques. This article analyzes the process of query construction and illustrates, through examples, how to transform … Read more
Recently, while reading some materials about RAG systems, I discovered an interesting phenomenon: the relevance issue of RAG is far more complex than we imagine. Whether from the perspective of data retrieval or the understanding of relevance by large models, the performance of RAG is filled with challenges and opportunities. Today, I would like to … Read more
Introduction Many product managers and engineers familiar with RAG often complain, “It only takes a week to produce a demo with RAG, but it takes at least six months to reach a production-level standard!” This is a realistic issue for the current industrial implementation of RAG. The RAG framework is very simple and understandable, and … Read more