Building an Agentic RAG Support Assistant with Elastic & Jina

We built an agentic RAG support assistant using Elasticsearch, Jina, and Ollama. It understands natural language questions, retrieves the right docs, reranks them, and returns answers with sources. Here's how it works and how to run it. The Problem Priya is a support engineer drowning in tickets. Every morning she opens her dashboard to forty-plus questions from customers. Half of them have answers buried somewhere in the company's knowledge base—thousands of pages of docs, FAQs, and troubleshooting guides. The problem is the search box. It uses keyword matching. When a customer writes "My dashboard shows yesterday's data," the search returns articles about "data export" and "dashboard customization." Technically it found the words "dashboard" and "data." But it completely missed the intent. Priya ends up manually hunting through docs, wasting twenty minutes per ticket on information that should be instant. Multiply that across a fifteen-person support team and you're burning fifty hou