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所在平台: Udemy |
课程主页: https://www.udemy.com/course/langchain-for-developers-using-openai-llms-in-python/
课程评论：没有评论
课程名称：LangChain与生成式AI：在Python中使用OpenAI LLMs 概述：本课程旨在赋能开发者，提供一套实用的指南，帮助将LangChain与OpenAI有效集成，并在Python中使用大型语言模型（LLMs）。课程的初始阶段，您将深入了解LangChain的定义、功能和组件，以及它如何与数据源和LLMs协同工作。我们将简要探讨LLMs的架构、训练过程及其应用实例，并通过Google Colab提供环境设置的安装指南和“Hello World”示例。 随后，我们将探索LangChain模型，涵盖多种类型，包括LLMs、聊天模型和嵌入模型。我们将指导您加载OpenAI聊天模型，连接LangChain与Huggingface Hub模型，以及利用OpenAI的文本嵌入。 课程进一步深入LangChain中的提示和解析的基本方面，聚焦于最佳实践、分隔符、结构化格式以及示例与思维链推理（CoT）的有效使用。 接下来的部分将重点介绍LangChain中的内存、链式处理和索引等概念，帮助您轻松处理复杂交互。我们将研究如何调整聊天机器人的内存、链式处理的重要性以及文档加载器与向量存储的实用性。 最后，您将深入了解LangChain代理的实际实施，包括一个简单代理的演示以及构建Arxiv总结代理的逐步指导。 完成本课程后，您将熟练掌握在Python中使用LangChain与OpenAI LLMs，标志着您开发者旅程的重大飞跃。准备好提升您的LLM应用吗？欢迎加入我们这个全面的课程！
This course is designed to empower developers, this comprehensive guide provides a practical approach to integrating LangcChain with OpenAI and effectively using Large Language Models (LLMs) in Python.In the course's initial phase, you'll gain a robust understanding of what Langchain is, its functionalities and components, and how it synergizes with data sources and LLMs. We'll briefly dive into understanding LLMs, their architecture, training process, and various applications. We'll set up your environment with a hands-on installation guide and a 'Hello World' example using Google Colab.Subsequently, we'll explore the LangChain Models, covering different types such as LLMs, Chat Models, and Embeddings. We'll guide you through loading the OpenAI Chat Model, connecting LangChain to Huggingface Hub models, and leveraging OpenAI's Text Embeddings.The course advances to the essential aspect of Prompting & Parsing in LangChain, focusing on best practices, delimiters, structured formats, and effective use of examples and Chain of Though Reasoning (CoT).The following sections focus on the concepts of Memory, Chaining, and Indexes in LangChain, enabling you to handle complex interactions with ease. We will study how you can adjust the memory of a chatbot, the significance of Chaining, and the utility of Document Loaders & Vector Stores.Finally, you'll delve into the practical implementation of LangChain Agents, with a demonstration of a simple agent and a walkthrough of building an Arxiv Summarizer Agent.By the end of this course, you'll have become proficient in using LangChain with OpenAI LLMs in Python, marking a significant leap in your developer journey. Ready to power up your LLM applications? Join us in this comprehensive course!
