https://github.com/thomasvitale/java-ai-workshop

This hands-on workshop will guide you in building Java applications enhanced with AI capabilities by leveraging Generative AI and Large Language Models (LLMs), using Spring Boot and Spring AI.
https://github.com/thomasvitale/java-ai-workshop

generative-ai java large-language-models llm ollama opentelemetry spring-ai spring-boot testcontainers

Last synced: about 2 months ago
JSON representation

This hands-on workshop will guide you in building Java applications enhanced with AI capabilities by leveraging Generative AI and Large Language Models (LLMs), using Spring Boot and Spring AI.

• Host: GitHub
• URL: https://github.com/thomasvitale/java-ai-workshop
• Owner: ThomasVitale
• License: apache-2.0
• Created: 2024-09-26T15:43:51.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-10-27T16:11:02.000Z (3 months ago)
• Last Synced: 2024-10-27T19:00:32.640Z (3 months ago)
• Topics: generative-ai, java, large-language-models, llm, ollama, opentelemetry, spring-ai, spring-boot, testcontainers
• Language: Java
• Homepage:
• Size: 375 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# AI Applications with Java and LLMs

This hands-on workshop will guide you in building Java applications enhanced with AI capabilities by leveraging

Generative AI and Large Language Models (LLMs). Using Spring Boot and Spring AI, the workshop will cover a range

of techniques and patterns to implement use cases, such as conversational chats, question answering with documents,

structured data extraction, semantic search, agents, and more. Examples will include integrations with Ollama,

Mistral AI, OpenAI, Anthropic, Groq, and Hugging Face.

## Agenda

* Introduction to Generative AI and Large Language Models (LLMs). 

* Architectures of LLM applications, the role of Java and AI frameworks (Spring AI, LangChain4j). 

* Integrating applications with chat models, prompt design, and structured outputs. 

* Integrating applications with embedding models, ETL pipelines for reading, transforming, and loading documents into vector stores. 

* Implementing the Retrieval Augmented Generation (RAG) pattern, semantic search, and vector stores. 

* Integrating applications with other models (image, speech, moderation). 

* Evaluation, observability, and automated tests for LLM-powered applications. 

* Developer experience with Testcontainers and Ollama.

## Pre-requisites

* Familiarity with Java and core Spring Boot.

* Laptop with a Java IDE and Docker Desktop/Podman Desktop installed.

* A GitHub personal account.

## Additional Resources

* [Spring AI Documentation](https://spring.io/projects/spring-ai)

* [Spring AI Examples](https://github.com/ThomasVitale/llm-apps-java-spring-ai)