Translations:FACTS About Building Retrieval Augmented Generation-based Chatbots/7/zh: Difference between revisions

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Chat-GPT’s release, the emergence of vector databases, and the widespread use of retrieval augmented generation (RAGs) ([[#bib.bib8|8]]) marked the beginning of a new era in the Chatbot domain. Now, LLMs can understand user intents with simple prompts in natural language, eliminating the need for complex intent variant training, synthesize enterprise content coherently, thereby empowering chatbots with conversational capability beyond scripted intent recognition. While LLMs bring their generative capabilities to construct coherent, factual, and logical responses to user queries, vector database-powered information retrieval (IR) systems augment LLMs ability to retrieve fresh content. Tools like LangChain ([[#bib.bib1|1]]) and Llamaindex ([[#bib.bib9|9]]) facilitate chatbot construction, and orchestration of complex workflows including memory, agents, prompt templates, and overall flow. Together, vector-search based IR systems, LLMs, and LangChain-like frameworks form core components of a RAG pipeline and are powering generative AI chatbots in the post Chat-GPT era.

Chat-GPT 的发布、向量数据库的出现以及检索增强生成（RAGs）的广泛使用（8）标志着聊天机器人领域新时代的开始。现在，大型语言模型（LLMs）可以通过自然语言中的简单提示理解用户意图，消除了复杂意图变体训练的需求，能够连贯地合成企业内容，从而赋予聊天机器人超越脚本化意图识别的对话能力。虽然 LLMs 通过其生成能力构建连贯、真实和逻辑的用户查询响应，但由向量数据库驱动的信息检索（IR）系统增强了 LLMs 检索新鲜内容的能力。像 LangChain（1）和 Llamaindex（9）这样的工具促进了聊天机器人的构建，并协调包括记忆、代理、提示模板和整体流程在内的复杂工作流。向量搜索为基础的 IR 系统、LLMs 和类似 LangChain 的框架共同构成了 RAG 管道的核心组件，并在后 Chat-GPT 时代推动了生成式 AI 聊天机器人的发展。