Build a Basic RAG System with LangChain | MiniLM Embeddings + Recursive Text Splitter Explained

Description

🚀 In this video, I’ll show you how to build a **Basic RAG (Retrieval-Augmented Generation) System** from scratch using **LangChain**, **Hugging Face Embeddings**, and **LangChain Expression Language (LCEL)**. We’ll walk step-by-step through how RAG works — from **chunking your documents** using a Recursive Text Splitter to **embedding them with MiniLM**, and finally, **querying them intelligently** with LCEL. 🧩 What You’ll Learn: - Understanding the architecture of a RAG system - Using **Recursive Text Splitter** to create optimized document chunks - Applying **Hugging Face all-MiniLM-L6-v2 embeddings** for vectorization - Implementing **LangChain Expression Language (LCEL)** for chaining logic - Connecting retrieval and generation seamlessly - Running and testing your RAG query pipeline 🧠 Tools & Libraries Used: - **LangChain** - **LangChain Expression Language (LCEL)** - **Hugging Face all-MiniLM-L6-v2 Model** - **RecursiveCharacterTextSplitter** - **Chroma / FAISS VectorStore** - **Python** 📘 What is RAG? Retrieval-Augmented Generation combines **information retrieval (search)** and **generation (LLMs)** — allowing your model to give more accurate, context-aware answers. It’s the foundation of advanced AI applications like **ChatGPT with custom knowledge** or **AI assistants**. ⚙️ Project Flow: 1️⃣ Text Loading & Preprocessing 2️⃣ Recursive Text Splitting 3️⃣ Embedding using MiniLM 4️⃣ Storing vectors in Chroma/FAISS 5️⃣ Query Processing via LCEL 6️⃣ Response Generation 💡 By the end of this video, you’ll have your own **fully functional RAG system** running locally — scalable and ready for integration into any AI app. --- 🔥 Don’t forget to **like**, **comment**, and **subscribe** for more AI & LangChain tutorials! #RAG #LangChain #HuggingFace #MiniLM #LLM #AIProjects #AIAgent #MachineLearning #NLP #LCEL