A Production-Ready RAG Knowledge Base in ~500 Lines of Spring Boot and LangChain4j

LangChain4j’s Spring Boot starter and BOM turn a RAG pipeline from a sprawling Python service into a single JVM module that fits existing Java shop infrastructure. The multi-knowledge-base isolation pattern shown here — one metadata field, one filter — is the simplest way to avoid cross-tenant leakage without standing up separate collections.

The system chains Apache Tika for document parsing, a sliding-window chunker with 200-character overlap, and DashScope’s text-embedding-v3 model for 1024-dimension vectors stored in Milvus. Multi-tenancy comes from a `knowledgeBaseId` metadata filter on every chunk, so HR, engineering, and product knowledge bases stay isolated inside a single vector store. Retrieval uses a Top-5 similarity search with a 0.6 minimum score, and the final prompt instructs the model to answer only from the provided context.

Both synchronous and streaming (SSE) endpoints are exposed. A content-safety layer blocks prompt-injection patterns and enforces input length limits, while a unified exception handler and standard `Result<T>` envelope keep the API predictable. The whole pipeline averages 800–1200 ms latency and 500–600 tokens per query.

A tuning table maps chunk size, overlap, Top-K, minScore, and temperature to their effects on precision, recall, and cost, with concrete remedies for common failure modes like irrelevant retrieval or answers taken out of context.