RAG Without the Chatbot: pgvector + Ollama for Operational Data Most RAG tutorials start with "upload a PDF and ask questions about it." That's fine for document search. But I needed RAG for something different: diagnosing failures in a distributed system by searching through historical saga events. No PDFs. No chatbot. Just a Kafka consumer that vectorizes every saga event into pgvector and an agent that searches similar past incidents to diagnose new failures. This series covers how I built it. The stack is Ollama for local embeddings, pgvector on PostgreSQL for storage, and LangChain4j to tie it together. Why RAG (and Not Just Logs) My saga orchestrator processes orders across 5 microservices. When a saga fails, the event carries a full history: which services ran, what status each returned, what error messages were generated. This data lives in Kafka and MongoDB. I could search logs. But logs are text. Searching "payment failed" gives you exact matches.…