Build a Zero-Cost RAG Pipeline for PDFs (FAISS + Hugging Face)

Build a retrieval-augmented generation pipeline without paying for tools. Turn PDFs into grounded answers using a simple RAG workflow. This video breaks down how to build a zero-cost RAG engine that can read PDF documents, retrieve the most relevant chunks for a query, and generate a response that uses that retrieved context. The focus is on practical steps: chunking, embeddings, vector search, and generation. This is for US learners building AI prototypes, data/ML students, and developers who want document Q&A without relying on paid vector databases. It helps solve the common problem of LLMs giving generic answers by grounding outputs in specific PDF content. Topics covered include PDF text extraction with PyMuPDF, chunking strategies for better retrieval, creating embeddings with Sentence Transformers (all-MiniLM-L6-v2), indexing and similarity search with FAISS, and a retrieve-and-generate flow that uses GPT-2 to produce a final response using the query plus retrieved context. Learn more with the full course: https://www.mygreatlearning.com/academy/learn-for-free/courses/introduction-to-rag?utm_source=CPV_YT&utm_medium=Desc&utm_campaign=build_a_zero_cost_rag_pipeline_for_pdfs_faiss_hugging_face Chapters: 00:00 Build a zero-cost RAG engine (overview) 00:39 Key objectives of the RAG pipeline 00:57 Embeddings and indexing with FAISS 01:14 Query processing and response generation (GPT-2) 01:40 End-to-end integration (complete RAG flow) 01:54 Step 1: Install and import required libraries 02:55 Load a PDF and chunk the text 03:39 Create embeddings (MiniLM) and build the FAISS index 04:07 Retrieve top chunks and generate grounded answers #RAG #GenerativeAI #MachineLearning