What you'll learn:
- Learn what Spring AI is how it simplifies using LLMs in our applications
- Use OpenAI LLMS in a Spring Boot application
- Use Open Source LLMS like Mistral,Gemma in a Spring Boot application
- Run Open Source LLMs on your local machine using OLLAMA
- Use PromptTemplates to reuse and build dynamic prompts
- Learn why and how to maintain Chat History
- Learn what embeddings are and use the Embeddings Model to find text Similarity
- Understand what a Vector Store is and use it to store and retrieve Embeddings
- Understand the process of Retrieval Augmented Generation(RAG)
- Implement (RAG) to use our own data with LLMs in simple steps
- Analyze images using Multi Modal Models
- Build multiple LLM APPs using ThymeLeaf and Spring AI
- Master Function Calling and Text Moderations
- All in simple steps
Welcome to Spring AI for Beginners!
This course is designed to provide a gentle, step-by-step introduction to Spring AI, guiding you
from the basics to more advanced concepts. Whether you're a complete novice or have some
experience with AI, this course will help you understand and leverage the power of Spring AI for
building AI-powered applications.
Course Goals:
- Gradual Learning: Learn Spring AI gradually from basic to advanced topics with clear and
concise instructions.
- Comprehensive Understanding: Understand why Spring AI is a powerful tool for building AI
applications and how it simplifies the integration of language models into your projects.
- Hands-On Experience: Gain practical experience with essential Spring AI features such as
prompt templates, chains, agents, document loaders, output parsers, and model classes.
What You Will Learn:
- Introduction to Spring AI: Get started with the basics of Spring AI and understand its core
concepts.
- Building Blocks of Spring AI: Learn about prompt templates, chains, agents, document loaders,
output parsers, and model classes.
- Creating AI Applications: See how these features come together to create a smart and flexible
- Practical Coding: Write and run code examples to get a hands-on sense of how Spring AI
development looks like.
Course Structure:
- Concise Chapters: Each chapter focuses on a specific topic in Spring AI programming,
ensuring you gain a deep understanding of each concept.
- Interactive Learning: Code along with the examples provided to reinforce your learning and build
your skills.
By the end of this course, you will:
Learn what Spring AI is how it simplifies using LLMs in our applications
Use OpenAI LLMs in a Spring Boot application
Use Open Source LLMs like Mistral,Gemma in a Spring Boot application
Run Open Source LLMs on your local machine using OLLAMA
Use PromptTemplates to reuse and build dynamic prompts
Learn why and how to maintain Chat History
Learn what embeddings are and use the Embeddings Model to find text Similarity
Understand what a Vector Store is and use it to store and retrieve Embeddings
Understand the process of Retrieval Augmented Generation(RAG)
Implement (RAG) to use our own data with LLMs in simple steps
Analyze images using Multi Modal Models
Build multiple LLM APPs using Thymeleaf and SpringAI
All in simple steps
