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所在平台: Udemy |
课程主页: https://www.udemy.com/course/generative-ai-architectures-with-llm-prompt-rag-vector-db/
课程评论：没有评论
课程名称：生成式 AI 架构与大语言模型（LLM）、提示工程、RAG、向量数据库 课程简介： 本课程将教您如何设计生成式 AI 架构，将 AI 驱动的小型及大型语言模型（S/LLMs）集成到电子商务支持企业应用中，使用提示工程、RAG（检索增强生成）、微调与向量数据库。我们将设计的生成式 AI 架构包括以下组件：小型和大型语言模型（S/LLMs）、提示工程、检索增强生成（RAG）、微调和向量数据库。 课程内容逐渐深入，从基础知识开始，教授 LLM 增强流程的强大框架，遵循提示工程、RAG 和微调的步骤。 主要模块包括： 1. **大型语言模型（LLMs）**： - LLM 的工作原理及其文本生成、摘要、问答、分类、情感分析、语义搜索嵌入和代码生成能力。 - 使用 ChatGPT 进行文本生成，理解 LLM 的能力和限制。 - LLM 模型示例：OpenAI ChatGPT、Meta Llama、Anthropic Claude 等。 - 本地安装和运行 Llama 和 Gemma 模型。 2. **提示工程**： - 有效提示的设计步骤：迭代、评估和模板化。 - 高级提示技术：零-shot、一次性-shot、几个-shot、连锁思维等。 - 为电子商务支持设计的高级提示。 3. **检索增强生成（RAG）**： - RAG 架构的组成部分，包括嵌入与向量搜索的摄取、重新排序和上下文查询提示。 - 设计基于 RAG 的电子商务客户支持。 4. **微调**： - 微调的工作流程和方法，包括全微调和参数高效微调（PEFT）。 - 使用微调提升电子商务客户支持的效果。 5. **向量数据库与语义搜索**： - 向量、向量嵌入与向量数据库的基本概念。 - 向量数据库的工作机制，包括创建、索引和搜索。 - 探索不同的向量数据库，例如 Pinecone、Chroma 等。 最后，我们将结合 LLM 和向量数据库设计电子商务支持架构，并将其作为云原生服务集成到微服务架构中。本课程不仅是学习生成式 AI 的过程，更是深入了解如何将高级 AI 解决方案设计应用于企业的实践。在课程结束时，您将获得设计完整电子商务客户支持应用的实践经验，应用 LLM 能力进行摘要、问答、分类和情感分析等功能。
In this course, you'll learn how to Design Generative AI Architectures with integrating AI-Powered S/LLMs into EShop Support Enterprise Applications using Prompt Engineering, RAG, Fine-tuning and Vector DBs.We will design Generative AI Architectures with below components;Small and Large Language Models (S/LLMs)Prompt EngineeringRetrieval Augmented Generation (RAG)Fine-TuningVector DatabasesWe start with the basics and progressively dive deeper into each topic. We'll also follow LLM Augmentation Flow is a powerful framework that augments LLM results following the Prompt Engineering, RAG and Fine-Tuning.Large Language Models (LLMs) module;How Large Language Models (LLMs) works?Capabilities of LLMs: Text Generation, Summarization, Q & A, Classification, Sentiment Analysis, Embedding Semantic Search, Code GenerationGenerate Text with ChatGPT: Understand Capabilities and Limitations of LLMs (Hands-on)Function Calling and Structured Output in Large Language Models (LLMs)LLM Models: OpenAI ChatGPT, Meta Llama, Anthropic Claude, Google Gemini, Mistral Mixral, xAI GrokSLM Models: OpenAI ChatGPT 4o mini, Meta Llama 3.2 mini, Google Gemma, Microsoft Phi 3.5Interacting Different LLMs with Chat UI: ChatGPT, LLama, Mixtral, Phi3Interacting OpenAI Chat Completions Endpoint with CodingInstalling and Running Llama and Gemma Models Using Ollama to run LLMs locallyModernizing and Design EShop Support Enterprise Apps with AI-Powered LLM CapabilitiesPrompt Engineering module;Steps of Designing Effective Prompts: Iterate, Evaluate and TemplatizeAdvanced Prompting Techniques: Zero-shot, One-shot, Few-shot, Chain-of-Thought, Instruction and Role-basedDesign Advanced Prompts for EShop Support - Classification, Sentiment Analysis, Summarization, Q & A Chat, and Response Text Generation Design Advanced Prompts for Ticket Detail Page in EShop Support App w/ Q & A Chat and RAGRetrieval-Augmented Generation (RAG) module;The RAG Architecture Part 1: Ingestion with Embeddings and Vector SearchThe RAG Architecture Part 2: Retrieval with Reranking and Context Query PromptsThe RAG Architecture Part 3: Generation with Generator and OutputE2E Workflow of a Retrieval-Augmented Generation (RAG) - The RAG WorkflowDesign EShop Customer Support using RAGEnd-to-End RAG Example for EShop Customer Support using OpenAI PlaygroundFine-Tuning module;Fine-Tuning WorkflowFine-Tuning Methods: Full, Parameter-Efficient Fine-Tuning (PEFT), LoRA, TransferDesign EShop Customer Support Using Fine-TuningEnd-to-End Fine-Tuning a LLM for EShop Customer Support using OpenAI PlaygroundAlso, we will discussChoosing the Right Optimization - Prompt Engineering, RAG, and Fine-TuningVector Database and Semantic Search with RAG moduleWhat are Vectors, Vector Embeddings and Vector Database? Explore Vector Embedding Models: OpenAI - text-embedding-3-small, Ollama - all-minilm Semantic Meaning and Similarity Search: Cosine Similarity, Euclidean Distance How Vector Databases Work: Vector Creation, Indexing, Search Vector Search Algorithms: kNN, ANN, and Disk-ANN Explore Vector Databases: Pinecone, Chroma, Weaviate, Qdrant, Milvus, PgVector, RedisLastly, we will Design EShopSupport Architecture with LLMs and Vector DatabasesUsing LLMs and VectorDBs as Cloud-Native Backing Services in Microservices Architecture Design EShop Support with LLMs, Vector Databases and Semantic Search Azure Cloud AI Services: Azure OpenAI, Azure AI Search Design EShop Support with Azure Cloud AI Services: Azure OpenAI, Azure AI SearchThis course is more than just learning Generative AI, it's a deep dive into the world of how to design Advanced AI solutions by integrating LLM architectures into Enterprise applications. You'll get hands-on experience designing a complete EShop Customer Support application, including LLM capabilities like Summarization, Q & A, Classification, Sentiment Analysis, Embedding Semantic Search, Code Generation.
