Master RAG with Langchain 🦜⛓️‍💥

Your Comprehensive Guide to Retrieval-Augmented Generation!

Discover how to harness the power of Retrieval-Augmented Generation (RAG) using LangChain in this comprehensive tutorial series.

Why RAG Matters

In the rapidly evolving AI landscape, RAG stands out as a cutting-edge technique that:

• Enhances generative models with external knowledge sources
• Improves content quality and accuracy
• Grounds responses in reliable data

What You'll Learn

This step-by-step guide covers:

1. RAG Pipeline Basics: Understand the foundation of combining retrieval systems with generative models.
2. Query Transformation: Refine user queries for optimal language model processing.
3. Hypothetical Document Embeddings: Generate vector representations to assess document relevance.
4. Intelligent Routing: Select the most appropriate data sources for each query.
5. Advanced Techniques:
   • Executable query construction
   • Effective indexing strategies
   • Retrieval techniques: Self RAG, Adaptive RAG, and CRAG (Conditional Retrieval-Augmented Generation)

Practical Application

Apply your knowledge by building a hospital management system, demonstrating RAG's real-world potential.

Who Is This For?

• Beginners looking to understand RAG
• Experienced practitioners aiming to refine their skills

Get Started

Embark on your journey to master Retrieval-Augmented Generation with LangChain. Each tutorial builds on the previous, providing you with a solid foundation and advanced techniques.

This series is inspired by the LangChain Tutorial Series, enhanced with additional insights and improvements.