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所在平台: Udemy |
课程主页: https://www.udemy.com/course/langchain-on-azure-building-scalable-llm-applications/
课程评论：没有评论
课程名称：Azure上的LangChain - 构建可扩展的LLM应用 课程概述：本课程深入探讨Azure平台与大型语言模型（LLM）应用，提供全面的学习体验。课程从Azure账户结构和资源组的初始设置开始，逐步引导学员掌握Azure Blob存储的管理，培养在Azure这一广泛服务环境下导航和利用的基本技能。接着，我们将研究不同的向量存储解决方案，如Azure认知搜索和PgVector，并比较它们的优缺点。学员将学习如何对原始数据进行分块、嵌入并插入向量存储。同时，课程将通过Jupyter笔记本实践执行典型的增强检索生成（RAG）过程。 在掌握基础知识后，我们将从笔记本转向使用docker-compose在本地启动服务，深入了解这些服务的工作原理。接下来，课程将教授如何将这些服务部署到云平台，包括学习新的服务如容器注册表和应用服务。一旦设置了Web应用程序，我们将实现基于事件的索引流程，利用Blob触发器、事件网格和Azure Functions，根据Blob存储中的更改实时索引文档。 最后几章将介绍基本的安全措施，例如为数据库设置防火墙和基于IP的访问限制。此课程专为拥有Python、Docker和LangChain基础知识的学员设计，适合那些希望构建具有生产级架构的真实应用，超越使用Streamlit进行简单实验型应用的学习者。
Dive into the depths of Azure and Large Language Model (LLM) applications with this comprehensive course. Starting with the initial setup of Azure account structures and resource groups, moving to the practical management of Azure Blob Storage, this course equips you with the essential skills to navigate and utilize Azure's extensive offerings.We then delve into different vector stores, such as Azure Cognitive Search and PgVector, comparing their advantages and disadvantages. You will learn how to chunk raw data, embed it, and insert it into the vector store. A typical Retrieval Augmented Generation (RAG) process is performed on the vector store, primarily using Jupyter notebooks for this part of the course.After covering the basics, we transition from notebooks to using docker-compose to locally spin up services. We'll delve deeply into how these services work.The next step is deploying these services to the cloud, where we learn about new services like the Container Registry and App Service.Once the Web Apps are set up, we implement an event-driven indexing process with Blob Triggers, the Event Grid, and Azure Functions to index documents upon changes in Blob Storage.The final chapters cover basic security measures, such as setting up a firewall for the database and IP-based access restrictions.This course is tailored for individuals with foundational knowledge of Python, Docker, and LangChain and is perfect for anyone looking to build real applications with a production-grade architecture, moving beyond simple playground apps with Streamlit.
