Tutorials > Spring Ai: Creating Workflows, Agents And Parsing Data

Spring Ai: Creating Workflows, Agents And Parsing Data

Date: 30 May 2025 | Posted by: 0nelove

Spring Ai: Creating Workflows, Agents And Parsing Data
Published 5/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.40 GB | Duration: 2h 9m
Creating Workflows, Agents and Parsing Data create intelligent workflows, autonomous agents, and advanced data parsing

What you'll learn
Build AI-driven workflows and automation using the Spring framework and generative AI models (LLMs)
Develop intelligent AI agents in Java that can interact with APIs and data, leveraging Spring AI's tools
Parse and analyze data with AI (NLP techniques) integrated into Spring Boot applications
Integrate OpenAI/ChatGPT and other models into Spring Boot to create real-world AI-powered features
Apply best practices in AI app development, including prompt engineering, model selection, and deployment in Java
Requirements
Intermediate Java programming experience (familiarity with Java 11+ syntax and OOP)
Basic knowledge of the Spring Boot framework (creating simple REST APIs, Spring Boot project setup)
Description
In this course, you won't waste hours watching unfocused coding or endless trial and error. Every lesson is designed to deliver practical knowledge and clear explanations, so you can make real progress without unnecessary filler. Better a shorter and direct curse than tons of hours without giving you time to practise.If you are an intermediate Java developer eager to start creating products with AI, this course is your gateway to building intelligent applications with Spring. Are you comfortable with Spring Boot and looking to add cutting-edge AI features like chatbots, workflow automation, or smart data processing to your skillset? This course blends theory with hands-on projects to take your expertise to the next level. You'll learn how to harness Spring AI – the latest Spring ecosystem project – to seamlessly integrate powerful AI models (like OpenAI's GPT-4) into Java applications.In "Spring AI: Creating Workflows, Agents and Parsing Data," you will work on real-world scenarios and coding exercises that bridge the gap between AI and Spring development. Through a step-by-step approach, you'll:Develop AI-driven workflows: use Spring Boot and generative AI APIs to automate tasks and decision-making processes in your apps.Build autonomous AI agents: create agents that can call APIs, handle data, and make intelligent decisions (leveraging concepts like LangChain and Spring AI's tool integrations).Implement advanced data parsing: learn NLP techniques to extract insights from unstructured data (emails, documents, logs) using LLMs within Spring applications.Integrate popular AI models: bring ChatGPT, or other AI services into your Java projects, mastering API integration and prompt engineering.Why learn AI integration with Spring? Artificial Intelligence is transforming how software is built, and Java developers with AI skills are in high demand. By combining Spring Boot (Java's leading framework) with AI capabilities, you can build innovative, AI-powered products that stand out in the market. This course shows you practical techniques to add features like intelligent chatbots, automated workflows, and smart data analyzers to your applications – skills that can accelerate your career.
Overview
Section 1: Introduction
Lecture 1 Curse Overview and Support Material
Lecture 2 Why traditional computing fail on simple tasks
Lecture 3 Spring AI VS Native Library
Section 2: One-shot Prompt
Lecture 4 What is One-shot Prompt
Lecture 5 The Input and its parameters
Lecture 6 Choosing a LLM Provider and a model
Section 3: Retrieval, Tools & Prompt engineering
Lecture 7 Retrieval (RAG VS CAG)
Lecture 8 Adding Tool Calling
Lecture 9 Prompt engineering
Lecture 10 Avoiding Prompt Injection
Section 4: AI Workflow
Lecture 11 AI Workflows and How They Differ from Agents
Lecture 12 Workflow for Parsing Bills from CSV and PDF
Lecture 13 Add categories and suppliers in the workflow
Lecture 14 Add Support to PDF parsing
Lecture 15 PDF Processing with Image Extraction and Reasoning
Section 5: Agents & MCP
Lecture 16 Agent and MCP Integration Documentation
Lecture 17 Advanced Agents Overview
Section 6: Assistants
Lecture 18 Assistant Interaction
Lecture 19 Create a reactive end-point with tooling for assistant
Lecture 20 Front-End Assistant Code
Lecture 21 Code generation using V0
Section 7: Fine tunning
Lecture 22 Fine tunning a model
Section 8: Final Quiz
Software engineers and tech leads aiming to incorporate AI workflows and agents into enterprise Java applications

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