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

Описание: 🚀 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.

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