PART-1 | Naive RAG Explained | Build Retrieval-Augmented Generation Using Python & FastAPI

Learn Naive RAG (Retrieval-Augmented Generation) and understand how AI applications retrieve relevant information from documents and use an LLM to generate context-aware answers. In this session, explore: What is RAG, and why is it needed? Understanding the Naive RAG architecture Uploading and processing PDF documents Splitting documents into chunks Generating embeddings using Sentence Transformers Storing and searching vectors using FAISS Retrieving relevant document chunks Sending retrieved context to an LLM Building a RAG backend using Python and FastAPI Testing document-based question answering with practical examples 🔄 Naive RAG Workflow PDF Documents → Text Extraction → Chunking → Embeddings → Vector Database → Retrieval → LLM → Answer Understand how RAG helps LLMs answer questions using your own documents rather than relying only on their pretrained knowledge. VPro Skills — Learn, Practice & Build AI Skills! 🏷️ Best Hashtags #NaiveRAG #RAG #RetrievalAugmentedGeneration #GenerativeAI #LLM #Python #FastAPI #FAISS #VectorDatabase #Embeddings #SentenceTransformers #LangChain #AIEngineering #DocumentQA #MachineLearning #VProSkills