rag 知识库问答系统 这是一个基于 RAG (Retrieval-Augmented Generation) 架构的知识库问答系统，目前以运动鞋店铺知识库为例进行实现。 系统能够根据用户的问题，从知识库中检索相关信息，并结合大语言模型生成准确的回答。

In this guide, you will learn how to build a retrieval-augmented generation (RAG) chatbot application. Before we dive in, let's look at what RAG is, and why we would want to use it. What is RAG? RAG stands for retrieval augmented generation.

a RAG (Retrieval-augmented generation) ChatBot. The RAG Chatbot works by taking a collection of Markdown files as input and, when asked a question, provides the corresponding answer based on the context provided by those files.

Verba is a fully-customizable personal assistant utilizing Retrieval Augmented Generation (RAG) for querying and interacting with your data, either locally or deployed via cloud. Resolve questions around your documents, cross-reference multiple data points or gain insights from existing knowledge bases.

May 6, 2024 · In this comprehensive tutorial, you’ll discover: The key concepts behind RAG and how to use LangChain to create sophisticated chatbots. How to build both stateless and stateful (context-aware)...

Sep 16, 2024 · Learn how to build basic AI chatbot using Teams AI library in Teams Toolkit, RAG scenarios, data integration, Azure AI Search, and Microsoft 365 as data sources.

Jan 21, 2025 · Learn how to build powerful RAG chatbots with n8n's visual workflow automation. This step-by-step guide demonstrates how to connect to any knowledge source, index it in a vector database, and create an AI-powered chatbot …

Mar 6, 2025 · By combining generative AI with real-time data retrieval, RAG chatbots can provide smarter, more accurate responses. They bridge the gap between static AI and dynamic, real-time information. In this guide, you will learn what a RAG chatbot is, why it is different, and how you can build one yourself, without needing to be an AI expert.

RAG chatbots rely on a knowledge base that contains chunks of text. These chunks are preprocessed and converted into embedding vectors. Each chunk represents a piece of information that the chatbot can use to generate responses. 3. Semantic search. The heart of RAG chatbot architecture lies in semantic search.

In this tutorial, we will create a Retrieval-Augmented Generation (RAG) chatbot application using Taipy. Users can converse with the chatbot which responds with relevant and current information, through context provided via PDF files.