Build RAG from Scratch in Python: No Frameworks Needed

In the video ‘RAG from Scratch in 10 lines Python – No Framework’ by Prompt Engineering, the presenter demonstrates how to create a fully functional chat system using Retrieval Augmented Generation (RAG) with just 10 lines of Python code. The tutorial bypasses common frameworks like LangChain, LamaIndex, and vector stores such as Chroma, focusing instead on a straightforward Python implementation. The process involves setting up the Python environment, preparing and chunking data, embedding the chunks using a model, retrieving relevant chunks based on user queries, and generating responses with a language model (LLM). The presenter uses a Wikipedia article as an example document, chunks it into paragraphs, and embeds these chunks using an open-source embedding model. The user query is then embedded, and the most relevant document chunks are retrieved using dot product similarity. These chunks, along with the user query, are fed into the LLM to generate a response. The video emphasizes understanding the basic components of RAG before moving on to more complex systems using frameworks like LangChain.