{"id":14599,"date":"2026-08-10T11:16:06","date_gmt":"2026-08-10T11:16:06","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/udemy-building-a-rag-application-in-python-2026-6\/"},"modified":"2026-08-10T11:16:06","modified_gmt":"2026-08-10T11:16:06","slug":"udemy-building-a-rag-application-in-python-2026-6","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/udemy-building-a-rag-application-in-python-2026-6\/","title":{"rendered":"Udemy \u2013 Building a RAG application in Python 2026-6"},"content":{"rendered":"
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Description<\/h2>\n

Building a RAG application in Python is a Retrieval Augmentative Generation (RAG) course using Python and modern AI technologies published by Udemy Online Academy. This is a hands-on course that teaches developers how to build Retrieval Augmentative Generation (RAG) applications using Python and modern AI technologies. Students learn how to combine large language models (LLM) with external knowledge sources to produce more accurate, context-aware, and reliable answers. The course covers the entire RAG pipeline from data ingestion and document processing to vector embeddings, semantic search, retrieval mechanisms, and response generation.<\/p>\n

Build a powerful Retrieval Augmentative Generation (RAG) application in Python \u2013 from an empty directory to a streaming web chat with multi-threaded memory, hybrid retrieval, image retrieval, and two interchangeable backends for vector storage. No LangChain, no LlamaIndex, no magic. You write each line yourself and at the end you know exactly what each one does. You will build the pipeline from scratch \u2013 chunking, embeddings, auto-digestion, semantic-to-lexical hybrid retrieval with Reciprocal Rank Fusion, a query rewrite for subsequent queries, a token stream sent from the server, a visual model branch for images \u2013 on plain Postgres (with pgvector) and a local Ollama server.<\/p>\n

What you will learn in Building a RAG application in Python:<\/h3>\n