Course Code: ftrag
Duration: 14 hours
Prerequisites:
  • 理解自然语言处理（NLP）概念
  • 具备基于transformer的语言模型经验
  • 熟悉Python及基本机器学习工作流程

受众

  • NLP工程师
  • 知识管理团队
Overview:

Fine-Tuning 检索增强生成（RAG）系统的过程是优化大型语言模型如何从外部来源检索并生成相关信息，以用于企业应用。

这项由讲师指导的培训（线上或线下）针对中级NLP工程师和知识管理团队，他们希望微调RAG管道，以提升在问答、企业搜索和摘要应用场景中的性能。

在培训结束时，参与者将能够：

  • 理解RAG系统的架构和工作流程。
  • 针对特定领域数据微调检索器和生成器组件。
  • 评估RAG性能，并通过PEFT技术进行改进。
  • 部署优化的RAG系统以供内部或生产使用。

课程形式

  • 互动式讲座与讨论。
  • 大量练习与实践。
  • 在实时实验室环境中进行动手实作。

课程定制选项

  • 如需为此课程定制培训，请联系我们安排。
Course Outline:

检索增强生成（RAG）简介

  • 什么是RAG，以及它对企业AI的重要性
  • RAG系统的组件：检索器、生成器、文件存储
  • 与独立LLM和向量搜索的比较

设置RAG管道

  • 安装和配置Haystack或类似框架
  • 文件摄取和预处理
  • 将检索器连接到向量数据库（例如FAISS、Pinecone）

Fine-Tuning 检索器

  • 使用特定领域数据训练密集检索器
  • 使用句子转换器和对比学习
  • 通过top-k准确率评估检索器质量

Fine-Tuning 生成器

  • 选择基础模型（例如BART、T5、FLAN-T5）
  • 指令微调与监督微调的比较
  • 使用LoRA和PEFT方法进行高效更新

评估与优化

  • 评估RAG性能的指标（例如BLEU、EM、F1）
  • 延迟、检索质量和幻觉减少
  • 实验跟踪和迭代改进

部署与现实世界集成

  • 在内部搜索引擎和聊天机器人中部署RAG
  • 安全性、数据访问和治理考虑
  • 与API、仪表板或知识门户的集成

案例研究与最佳实践

  • 金融、医疗和法律领域的企业用例
  • 管理领域漂移和知识库更新
  • 检索增强LLM系统的未来方向

总结与下一步

Sites Published:

United Arab Emirates - Fine-Tuning for Retrieval-Augmented Generation (RAG) Systems

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