RAG 101: Learn how to build your first pipeline!

In this comprehensive tutorial, you’ll explore all of the essentials of Retrieval-Augmented Generation (RAG). You’ll learn how to combine large language models with your own documents using Python and LangChain, and build a complete pipeline from document ingestion to interactive Q&A. Whether you’re new to RAG or want a deeper understanding of LangChain components, this step-by-step guide covers all you need. Understand the fundamentals of Retrieval-Augmented Generation Learn about pipelines: document ingestion and querying Set up your environment, libraries, and API keys (OpenAI & Pinecone) Explore LangChain’s core components: LLMs, chains, memory, and document loaders Split and chunk documents for effective retrieval Generate embeddings and store them in FAISS or Pinecone Perform semantic search and similarity queries Build Pipeline 1: loading, chunking, and vector storage Build Pipeline 2: querying documents with RAG and GPT Add metadata, handle edge cases, and test with real queries 📌 Resources & Tutorials Code: https://colab.research.google.com/drive/1G93g11ul3s-9r3CH67OQJ7rSsM1pkhKo?usp=sharing Course: Build RAG Systems with LangChain (https://www.datacamp.com/courses/retrieval-augmented-generation-rag-with-langchain) Course: Developing LLM Applications with LangChain (https://www.datacamp.com/courses/developing-llm-applications-with-langchain) LangChain Documentation: https://python.langchain.com/ FAISS: https://github.com/facebookresearch/faiss Blog: Advanced RAG Techniques (https://www.datacamp.com/blog/rag-advanced) 📕 Chapters 00:00 Welcome & what is RAG? 02:15 Pipelines explained: ingestion & querying 07:14 Installing libraries & setting up API keys 11:09 LangChain basics: LLMs, chains & memory 18:28 Embeddings explained & vector representations 24:26 Loading documents: Wikipedia, web pages & PDFs 30:35 Chunking documents with text splitters 34:47 Vector databases: FAISS (local) vs. Pinecone (cloud) 41:40 Semantic search & building the retrie