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所在平台: Udemy |
课程主页: https://www.udemy.com/course/net-9-with-openai-complete-guide-with-real-time-project/
课程评论：没有评论
课程名称：.NET 9与OpenAI完整指南及实时项目 概述：该课程旨在介绍OpenAI及ASP.NET Core 9的基础知识，涵盖OpenAI提供的关键模型和功能，以及将OpenAI与ASP.NET Core 9结合的优势和收益。课程内容包括OpenAI API的工作原理、获取API密钥与身份验证、如何向OpenAI发送文本请求、创建基于聊天的应用程序并生成文本（如简单的问答机器人、内容生成等）。 课程将详细讲解IChatCompletionService的使用，该服务可将OpenAI的聊天补全模型（如GPT-4）集成到ASP.NET应用中，包括如何在MVC视图中呈现AI生成的结果。还将涵盖高级文本生成任务，如文本摘要、文本分类、情感分析，以及结构化数据生成（如HTML表格和JSON）。 此外，课程介绍了OpenAI图像处理和生成的基础知识，尤其是如何利用OpenAI的DALL·E模型根据文本描述生成图像。学员将学习如何通过OpenAI API生成响应查询的图像，并进行图像上传和分析。 动态响应部分将指导学员如何根据用户输入创建具体、结构化的响应，生成HTML，以及如何通过OpenAI生成结构化内容。该课程还包括使用OpenAI处理大型数据集的方法，查询与整合数据库，缓存与优化OpenAI请求等内容。 在处理文件上传（例如图像、CSV）时，课程将涵盖文件上传和处理的技巧，利用OpenAI分析文件，实现大型文件的高效处理。 课程的实战部分将引导学员构建一个集成了OpenAI的聊天机器人，涵盖多轮对话的处理以及聊天机器人的行为定制。 最后，课程中还考虑了安全性问题，如如何安全存储OpenAI API密钥、处理速率限制以及确保用户数据隐私。 该课程提供了一个全面的学习体验，适合希望将AI能力集成到Web应用程序中的开发者。
Introduction to OpenAI and ASP.NET Core 9Key Models and Capabilities Provided by OpenAIWhy Use OpenAI with ASP.NET Core 9? Benefits of Integrating AI Capabilities in Web ApplicationsSetup Requirements: Understanding How OpenAI API Works, Including Obtaining API Keys and AuthenticationMaking Text Requests to OpenAICreating Chat-Based Applications: Sending prompts to the API and generating text (simple Q & A bot, content generation, etc.).Using IChatCompletionService: Integrating OpenAI's chat completion models like GPT-4 into your ASP.NET application.Displaying AI-generated results: Rendering the output in an MVC view. Handling Advanced Text Generation TasksText summarization: Using OpenAI for summarizing large pieces of text.Text Classification: Sending data for text classification tasks.Sentiment Analysis: Analyzing the sentiment of user-submitted text using OpenAI.Generating Structured Data (Tables): Asking OpenAI to return results in specific formats (e.g., HTML tables, JSON). Image Handling and Generation with OpenAIIntroduction to DALL·E: Understanding how OpenAI's DALL·E model can generate images based on text descriptions.Integrating Image Generation: Using the OpenAI API to generate images (like product images or artwork) in response to queries.Image Upload and Analysis: Sending images to OpenAI for analysis (multimodal queries like GPT-4 Vision). Dynamic Responses Based on User InputUnderstanding Dynamic Responses: Crafting prompts that request specific, structured responses from OpenAI.Creating Custom AI Responses: Handling a variety of user queries dynamically (e.g., sales data, trend analysis).Generating HTML from OpenAI: Instructing OpenAI to generate HTML (tables, charts) in response to queries.Using AI to Generate Structured Content: Formatting AI's response into structured data, like HTML or CSV.Managing Large Datasets with OpenAIChunking large datasets: Dividing data into chunks and sending it to OpenAI for processing.Efficient Querying and Summarization: Using OpenAI's model to analyze large datasets in smaller, manageable pieces.Integrating OpenAI with Databases: Querying data from an SQL database (e.g., sales data) and passing it to OpenAI for processing.Caching and Optimizing OpenAI RequestsMinimizing OpenAI API Calls: Implementing caching for frequently asked queries to avoid redundant API requests.Storing Results Locally: Caching OpenAI responses in memory or a database.Using In-memory Data: Optimizing response times by calculating results in-memory before asking OpenAI for more complex analysis.Handling File Uploads (Images, CSV) with OpenAIUploading and Processing Files: Allowing users to upload images or CSV files and sending the data to OpenAI for analysis.Image and Document Analysis: Using OpenAI to analyze files (e.g., extracting text from an image or generating insights from CSV data).Storing Uploaded Files Temporarily: How to handle large file uploads and process them efficiently. Real-Time Application: Building a Chatbot with OpenAIIntegrating OpenAI into a Chatbot: Building a chatbot that responds to user queries using OpenAI's models.Handling Multi-turn Conversations: Maintaining state across multiple interactions with OpenAI to make conversations more natural.Customizing the Chatbot's Behavior: Tweaking the AI's personality, responses, and behavior according to user needs.Security ConsiderationsSecuring OpenAI API Keys: Properly securing API keys using environment variables or secret management services.Rate Limiting: Handling rate limiting by OpenAI's API to avoid hitting request limits.User Data Privacy: Ensuring that user input and any sensitive data are securely managed.
