💪 RAG example

This notebook demonstrates the creation and testing of a Retrieval-Augmented Generation (RAG) system using LangChain and Ollama. Below is a detailed breakdown of its structure and purpose.

Introduction

The notebook provides a step-by-step guide to building a simple RAG system. It involves:

  • Using a small corpus as the knowledge base for RAG operations.

  • Leveraging LangChain for retrieval and generation tasks.

  • Employing Ollama as the local large language model backend.

Dependencies

The required libraries are installed using the following commands:

pip install -qU langchain langchain_community
pip install -qU langchain_chroma
pip install -qU langchain_ollama

Additionally, if running in environments like Google Colab, the notebook includes special setups for using Ollama.

Notebook Overview

  1. Setting Up the Environment:

    • Installing dependencies.

    • Initializing the tools for retrieval and embedding.

  2. Loading the Corpus:

    • The notebook uses a small text-dataset as the source of knowledge.

    • It preprocesses the corpus into chunks for embedding.

  3. Building the RAG Pipeline:

    • Retrieve: Retrieves relevant chunks using semantic search.

    • Generate: Combines the retrieved context with the query and sends it to the LLM.

  4. Testing the System:

    • Users can input queries to test how the RAG pipeline responds.

    • The outputs are evaluated for relevance and accuracy.

Open In Colab