Tutorial_15_Retrieval_Augmented_Generation

Tutorial: Retrieval Augmented Generation (RAG)

This tutorial provides an in-depth guide on building applications using Retrieval Augmented Generation (RAG) to enhance the capabilities of Large Language Models (LLMs). By incorporating external knowledge retrieval, RAG addresses the inherent limitations of LLMs, allowing them to answer questions beyond their training data.

Overview

Large language models like GPT-4 and Llama-2 excel in generative reasoning but are restricted by the scope of their training datasets. RAG overcomes these limitations by integrating retrieval mechanisms, enabling dynamic knowledge augmentation from external sources.

Key Components

This tutorial demonstrates the use of the following components:

• LangChain: A framework for building applications involving LLMs.
• LangSmith: A tool for debugging and optimizing LangChain applications.
• Vector Stores: To store and retrieve document embeddings efficiently.

Features

• Installation Instructions: Steps to install required dependencies via Pip or Conda.
• LangSmith Integration: Guide on logging traces to debug workflows.
• Practical Example: A hands-on walkthrough of creating a RAG pipeline to answer questions about the contents of a website.

Example Workflow

The tutorial walks through:

1. Loading and preprocessing a webpage's content using LangChain's document loader.
2. Splitting text into manageable chunks using recursive character splitting.
3. Indexing and storing these chunks in a vector store for efficient retrieval.
4. Defining a retrieval and generation workflow using a state graph.
5. Executing the workflow to answer questions based on retrieved information.

Acknowledgments

• https://github.com/ray-project/llm-applications/blob/main/notebooks/rag.ipynb