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LLM and AI-Infused Applications with Java & Spring AI

Samples showing how to build Java applications powered by Generative AI and Large Language Models (LLMs) using Spring AI.

🛠️ Pre-Requisites

  • Java 23
  • Podman/Docker

💡 Use Cases

🧠 Models

💬 Chat Models

Chat completion with LLMs via different model providers:

🔤 Embedding Models

Vector transformation (embeddings) with LLMs via different model providers:

🎨 Image Models

Image generation with LLMs via different model providers:

🎵 Audio Models

Speech generation with LLMs via different model providers:

Speech transcription with LLMs via different model providers:

🛡️ Moderation Models

Coming soon

📐 Patterns

📝 Prompts, Messages, and Templates

Prompting using simple text:

Prompting using structured messages and roles:

Prompting using templates:

📊 Structured Output

Converting LLM output to structured JSON and Java objects:

🎯 Multimodality

Including various media in prompts with LLMs:

⚙️ Function Calling/Tools

Function calling with LLMs via different model providers:

🧠 Memory

Coming soon

📥 Data Ingestion

📚 Document Readers

Reading and vectorizing documents with LLMs via Ollama:

🔄 Document Transformers

Document transformation with LLMs via Ollama:

  • Metadata
    Enrich documents with keywords and summary metadata for enhanced retrieval.
  • Splitters
    Divide documents into chunks to fit the LLM context window.

🔢 Vector Stores

Coming soon

🔄 Retrieval Augmented Generation (RAG)

Question answering with documents using different RAG flows (with Ollama and PGVector):

➡️ Sequential RAG

🌳 Branching RAG

📊 Observability

🧠 LLM Observability

LLM Observability for different model providers:

🔢 Vector Store Observability

Vector Store Observability for different vector stores:

📋 Evaluation

Coming soon

🤖 Agents

Coming soon

📚 References and Additional Resources

🎤 Conferences

🎥 Videos

🎮 Demos

📚 Workshops