Conversational AI with Spring AI скачать в хорошем качестве

Conversational AI with Spring AI

Chapter 5 of the Apress ebook "Mastering Spring AI," titled "Conversational AI with Spring AI," focuses on developing advanced chat systems that go beyond simple rule-based chatbots. It describes how developers can leverage the capabilities of Large Language Models (LLMs) to create natural, context-aware, and personalized interactions. 1. Characteristics of Modern Conversational AI Unlike traditional chatbots, modern conversational AI is characterized by context understanding, adaptive learning, emotional intelligence (recognizing mood and tone of voice), and the ability to engage in multi-stage dialogues. Spring AI provides a robust platform for efficiently implementing such solutions. 2. Different Prompting Techniques This chapter provides a detailed overview of prompt types used in sales conversations or customer service: Direct vs. Indirect Prompting: Clear instructions versus subtle suggestions. Few-Shot vs. Zero-Shot: The model receives either a few examples or no prior information to solve a task. Persona-based Prompting: The model is assigned a specific role (e.g., "friendly technician"). Benefits: These techniques increase user engagement, improve data quality, and lead to more coherent conversations. 3. Implementation and Conversation History A significant portion of this chapter is dedicated to practical application. It explains how to build a chatbot with a frontend interface (HTML/JavaScript) and a Spring AI backend. Since AI models do not have a built-in "memory" of past interactions, this chapter demonstrates how to enable conversational history. This is achieved by collecting and sending previous user and AI messages (UserMessage and AssistantMessage) in a list to maintain context. 4. Advanced Methods: CoT and ReACT This chapter delves into two specialized problem-solving techniques: Chain of Thought (CoT): This method instructs the AI ​​to break down complex queries into smaller, logical steps, significantly increasing accuracy in challenging tasks such as technical support or financial planning. ReACT (Reason and Act): This method combines logical reasoning with action. The system follows a cycle of "Thought," "Action" (search or calculation), and "Observation" (observing the result) to arrive at a well-informed conclusion. #springai #conversationalai #apress #ebook