{"id":12860,"date":"2026-08-10T10:51:15","date_gmt":"2026-08-10T10:51:15","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/learn-data-engineering-local-genai-rag-with-llamaindex-ollama-2025-10\/"},"modified":"2026-08-10T10:51:15","modified_gmt":"2026-08-10T10:51:15","slug":"learn-data-engineering-local-genai-rag-with-llamaindex-ollama-2025-10","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/learn-data-engineering-local-genai-rag-with-llamaindex-ollama-2025-10\/","title":{"rendered":"Learn Data Engineering \u2013 Local GenAI RAG with LlamaIndex & Ollama 2025-10"},"content":{"rendered":"
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Descriptions<\/h2>\n

GenAI RAG with LlamaIndex, Ollama and Elasticsearch, Retrieval-Augmented Generation (RAG) is the next practical step after semantic indexing and search. In this course, you\u2019ll build a complete, local-first RAG pipeline that ingests PDFs, stores chunked vectors in Elasticsearch, retrieves the right context, and generates grounded answers with the Mistral LLM running locally via Ollama. We\u2019ll work end-to-end on a concrete scenario: searching student CVs to answer questions like \u201cWho worked in Ireland?\u201d or \u201cWho has Spark experience?\u201d. You\u2019ll set up a Dockerized stack (FastAPI, Elasticsearch, Kibana, Streamlit, Ollama) and wire it together with LlamaIndex so you can focus on the logic, not boilerplate. Along the way, you\u2019ll learn where RAG shines, where it struggles (precision\/recall, hallucinations), and how to design for production. By the end, you\u2019ll have a working app: upload PDFs \u2192 extract text \u2192 produce clean JSON \u2192 chunk & embed \u2192 index into Elasticsearch \u2192 query via Streamlit \u2192 generate answers with Mistral, fully reproducible on your machine.<\/p>\n

What you\u2019ll learn<\/h3>\n