构建基于RAG的聊天机器人:RAG融合(RAG Fusion) 代码参考:https://github.com/nimamahmoudi/LLMStreamlitDemoBasic 本文为译文,采用AI翻译,部分可能有误,原文参考: https://itnext.io/building-rag-based-chatbots-part-4-rag-fusion-e5612b556733itnext.io/building-rag-based-chatbots-part-4-rag-fusion-e5...
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这里的代码来自于github.com/Raudaschl/ra,整个流程就是: 查询改写—>多路搜索召回(内部逻辑没实现)—>倒数排序融合—>结果输出。 并没有重排步骤,以及基于搜索结果的生成过程。 import os import openai # 导入 openai 库 import random # 导入 random 库 # 初始化 OpenAI API openai.api_key = os.getenv("...
7.- [Rag Fusion --- Open Source Agenda] 8.- [rag-fusion/README.md at master · Raudaschl/rag-fusion (github.com)] 9.-[Analyzing RAG Methods: Semantic Similarity vs. Full Document Prompting | by Ingrid Stevens | Dec, 2023 | Medium] 10.- [limitations of RAG-Fusion --- Search (bi...
检索增强生成(RAG)显着先进了人工智能。它结合了预训练的密集检索和序列到序列模型的功能来生成响应。在此基础上,出现了一种称为RAG-Fusion的新方法,旨在...
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RAG-Fusion代码:https://github.com/Raudaschl/rag-fusion 让我们更详细地介绍每一个步骤。 多查询生成 为什么使用多查询? 在传统的搜索系统中,用户经常输入一个单一的查询来查找信息。虽然这种方法很简单,但它有局限性。单个查询可能无法捕获用户感兴趣的全部范围,或者它可能太窄而无法产生全面的结果。这就是从不...
RAG-Fusion代码:github.com/Raudaschl/ra 让我们更详细地介绍每一个步骤。 多查询生成 为什么使用多查询? 在传统的搜索系统中,用户经常输入一个单一的查询来查找信息。虽然这种方法很简单,但它有局限性。单个查询可能无法捕获用户感兴趣的全部范围,或者它可能太窄而无法产生全面的结果。这就是从不同角度生成多个查询...
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