So far, we have discussed the architecture of the Agent system, how to organize the system into sub-Agents, and how to build a unified mechanism to standardize communication. Today, we will turn our attention to the tool layer and one of the most important aspects you need to consider in Agent system design: data retrieval. … Read more
Click 👇🏻 to follow, article from 🙋♂️ Friends who want to join the community can see the method at the end of the article for group communication. “ LLMs have revolutionized the field of AI by enabling human-like text generation and natural language understanding. However, their reliance on static training data limits their ability to … Read more
Source: Knowledge Graph Technology This article has 2500 words and is recommended to read in 5 minutes. This article will help you understand how knowledge graphs can improve Retrieval-Augmented Generation (RAG) for information retrieval within companies. In this part of our AI series for knowledge management, you will learn how knowledge graphs enhance Retrieval-Augmented Generation … Read more
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