Spring Ai: Creating Workflows, Agents And Parsing Data

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