Building Intelligent RAG Applications with LangChain 🚀

Welcome to this guide on creating powerful Retrieval Augmented Generation (RAG) applications using LangChain and Python. RAG combines the power of large language models with custom knowledge bases to generate accurate, contextual responses.

What is RAG?

RAG (Retrieval Augmented Generation) is an AI framework that enhances language model outputs by incorporating relevant information from external knowledge sources. Instead of relying solely on the model's training data, RAG retrieves specific context to generate more accurate and factual responses.

Key Components

1. Document Loading: Ingesting documents from various sources
2. Text Splitting: Breaking documents into manageable chunks
3. Embeddings: Converting text into vector representations
4. Vector Store: Storing and retrieving similar documents
5. LLM Integration: Combining retrieved context with LLM capabilities

Getting Started with LangChain

Let's build a simple RAG application using LangChain. First, install the required packages: