Pure Java Enterprise AI/LLM Integration (EAI 2.0) by Adam Bien

Pure Java LLM integration requires minimal dependencies and can be packaged as a single JAR file, making deployment and installation straightforward

Local LLM models can run efficiently in Java without JNI dependencies thanks to projects like llama3.java and jllama, offering better cost control compared to cloud APIs

Enterprise LLM patterns mirror microservice patterns - circuit breakers, bulkheads, timeouts, retries are essential for handling throttling and reliability issues

LLMs should be treated as slow, unreliable, idempotent microservices with high latency; architecture should account for this

Cloud LLM APIs can become very expensive ($7+ per call) and face throttling issues - local models or hybrid approaches may be more cost-effective

Caching LLM responses and implementing proper prompt management are critical for controlling costs and ensuring consistency

LangChain4j provides enterprise integration features like vector databases, embedding models, and RAG (Retrieval Augmented Generation) capabilities

Testing LLM integrations requires new approaches like parameterized tests with different prompts and temperatures to evaluate responses

Enterprise concerns like compliance, traceability, and security drive architectural decisions around LLM integration

Java’s performance for LLM workloads is competitive with Python while offering better enterprise integration capabilities and deployment options