Build a RAG pipeline from scratch — the actually simple version

The article explains how to build a Retrieval-Augmented Generation (RAG) pipeline using only three libraries in Python, without any frameworks, cloud accounts, or configuration files. Retrieval-Augmented Generation is a technique where an LLM answers a question using text pulled from a private document store at query time, rather than relying on the model's pre-existing knowledge.

RAG is useful when source data changes frequently, is private, or is large, and requires citing specific passages. The article outlines four steps in a RAG system: chunking the source documents, embedding the chunks, storing the chunks and embeddings in a vector database, and finally retrieving and generating the answer. The article provides a step-by-step guide on setting up the required libraries, chunking the documents, embedding them, storing them in a Chroma vector database, and finally retrieving and generating the answer using the Claude API.

The example uses a list of short product documentation snippets as the source material, and demonstrates how to chunk the text into smaller passages, embed them using the sentence-transformers library, and store them in a local Chroma vector database. Finally, the article shows how to retrieve the closest chunks to a user's query and generate an answer using the Claude API.