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所在平台: Udemy |
课程主页: https://www.udemy.com/course/langgraph-with-ollama/
课程评论：没有评论
课程名称：2025年掌握LangGraph与LangChain以及Ollama - Agentic RAG 概述：本课程深入探索前沿的人工智能开发世界，提供有关LangGraph、Ollama和增强检索生成（RAG）技术的全面培训。无论是初学者还是专业人士，本课程都将帮助您掌握构建聊天机器人、在本地管理大型语言模型（LLM），并将强大的数据库查询能力无缝集成到您的项目中。通过逐步指导，您将学习到以下内容： 1. **Ollama本地LLM的设置与基准测试**：了解如何安装和配置Ollama以便与本地LLM配合使用，探索可用模型，进行基准测试，并使用强大的Ollama命令高效地管理和与AI模型互动。 2. **LangChain入门**：发现LangChain及其在应用程序中集成LLM的能力。这一部分提供了从安装到API调用的基础知识，使您能够利用LangChain构建智能系统。 3. **LangGraph基础知识**：掌握LangGraph，这是一种受状态机启发的AI系统设计工具。学习如何导航其图形和工具节点模块，创建能够通过图基编程增强功能的互动聊天机器人。 4. **LangGraph的类型提示与数据验证**：探索类型提示、数据验证和面向对象编程（OOP）原则在AI开发中的重要性。掌握TypedDict和Pydantic等工具，以编写干净、有效和可靠的项目代码。 5. **LangGraph中的图定义**：深入了解LangGraph中的图定义概念，以构建复杂系统。学习这些定义如何为您的AI工作流程带来清晰度和结构。 6. **结合LangGraph与Ollama的聊天机器人开发**：利用LangGraph和Ollama的强大功能构建功能齐全的聊天机器人。实现工具节点，设计稳健的系统架构，并为互动和智能用户对话添加记忆功能。 7. **Agentic文本到MySQL查询执行**：学习如何将LLM与MySQL集成，以实现无缝的查询执行。建立能够生成和执行数据库查询的智能代理，将结果连接到AI系统，并创建智能的数据库驱动工作流程。 8. **使用私有数据集的Agentic RAG**：掌握针对私有数据集的增强检索生成（RAG）技术。本部分教您如何准备数据集、创建嵌入、将其存储在向量数据库中，并实现能够实时检索和处理数据的RAG代理。 通过本课程，您将解锁自己的潜力，学习如何创建动态、具备记忆功能的聊天机器人，与私有数据集合作，并掌握针对AI应用程序的图形编程。
Take a deep dive into the world of cutting-edge AI development with this comprehensive course on LangGraph, Ollama, and Retrieval-Augmented Generation (RAG). Designed for beginners and professionals alike, this course equips you with the skills to build chatbots, manage LLMs locally, and integrate powerful database query capabilities seamlessly into your projects.With step-by-step guidance, you'll explore:Setting up and benchmarking local LLMs with Ollama.Building state-of-the-art chatbots using LangGraph and LangChain.Advanced type hinting, data validation, and OOPs principles for clean and efficient coding.Designing intelligent agents for MySQL queries and RAG workflows.Unlock your potential and learn how to create dynamic, memory-enabled chatbots, work with private datasets, and master graph-based programming for AI applications.Ollama Setup for Local LLMLearn how to install and configure Ollama to work with local LLMs. Explore available models, run benchmarks, and use powerful Ollama commands to manage and interact with AI models efficiently.Getting Started with LangChainDiscover LangChain and its capabilities for integrating LLMs into applications. From installation to API calls, this section provides foundational knowledge to leverage LangChain for building intelligent systems.LangGraph BasicsGain a clear understanding of LangGraph, a state-machine-inspired tool for designing AI systems. Learn to navigate its Graph and ToolNode modules, and create interactive chatbots that use graph-based programming for enhanced functionality.Type Hinting and Data Validation for LangGraphExplore the importance of type hinting, data validation, and OOP principles in AI development. Master tools like TypedDict and Pydantic to write clean, efficient, and reliable code for your projects.Graph Definitions in LangGraphDelve into the concept of graph definitions within LangGraph to build complex systems. Learn how these definitions bring clarity and structure to your AI workflows.Chatbot Development with LangGraph and OllamaCombine the power of LangGraph and Ollama to build feature-rich chatbots. Implement tool nodes, design robust system architectures, and add memory for interactive and intelligent user conversations.Agentic Text-to-MySQL Query ExecutionLearn to integrate LLMs with MySQL for seamless query execution. Build agents that generate and execute database queries, connect results to AI systems, and create intelligent database-driven workflows.Agentic RAG with Private DatasetsMaster Retrieval-Augmented Generation (RAG) for private datasets. This section teaches you to prepare datasets, create embeddings, store them in vector databases, and implement RAG agents capable of real-time data retrieval and processing.
