RAG 101: Learn how to build your first pipeline!

DataCamp · Beginner ·🔍 RAG & Vector Search ·6mo ago
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) E…
Watch on YouTube ↗ (saves to browser)

Chapters (9)

Welcome & what is RAG?
2:15 Pipelines explained: ingestion & querying
7:14 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

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Watch this before applying for jobs as a developer.
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Watch this before applying for jobs as a developer.
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