Developing Cloud-Native Java AI applications with DJL and LangChain4J by Sébastien Blanc, Alex Soto

Langchain4j provides Java developers with AI capabilities including model management, RAG (Retrieval Augmented Generation), tools/functions, and memory management

Deep Java Library (DJL) enables loading and inferencing AI models locally in Java applications without requiring Python dependencies

Vector databases like PGVector (Postgres extension) and Redis can be used to store embeddings for semantic search and RAG applications

Ollama allows running open source LLMs locally, similar to how Docker runs containers

AI applications often need stateful components like memory and conversation context which can be managed through databases or in-memory stores

Graph-based flows help orchestrate complex AI interactions and maintain application state between model calls

Tools/functions allow LLM models to call Java code directly, enabling integration with business logic

OpenTelemetry integration provides observability for AI applications including traces and metrics

Semantic caching improves performance by returning cached responses for semantically similar queries

Protection against prompt injection and other security concerns can be implemented through model validation and safety checks