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Build an LLM-powered Chatbot with RAG using LlamaIndex

PROJECT


Build an LLM-powered Chatbot with RAG using LlamaIndex

In this project, we’ll learn how to enhance large language model (LLM) applications with Retrieval Augmented Generation (RAG) using OpenAI, LlamaIndex, and Chainlit. We will develop an LLM-powered conversational assistant equipped with access to Wikipedia, allowing it to respond based on our chosen Wikipedia page(s).

Build an LLM-powered Chatbot with RAG using LlamaIndex

You will learn to:

Create a script to index Wikipedia pages in vector stores.

Create a custom Wikipedia semantic search tool.

Develop an intelligent, LLM-powered, Reason and Act (ReAct) agent.

Build a conversational UI.

Test your chat agent on a live Wikipedia page.

Skills

Artificial Intelligence

Interactive Real-time Web Applications

API Integration

Front-end Development

Prerequisites

Intermediate knowledge of object-oriented programming in Python

An OpenAI access token (API Key)

Understanding of ChatGPT or any other conversational AI tool (Bard, Claude 2, etc.)

Familiarity with large language models

Technologies

OpenAI

Python

Pydantic

chainlit logo

Chainlit

llamaindex logo

LlamaIndex

Project Description

In this project, we’ll discover how to enhance large language model (LLM) applications using retrieval-based augmentation (RAG). We’ll craft an LLM-powered conversational assistant equipped with access to Wikipedia, allowing it to respond based on our chosen Wikipedia page(s). We will employ cutting-edge LLM libraries and frameworks throughout this project, including OpenAI, LlamaIndex, and Chainlit. RAG, short for Retrieval Augmented Generation, helps improve the outputs of LLMs by adding factual information from a knowledge base.

Our conversational agent will operate with the ReAct prompt framework. This framework enables the agent to use tools step by step to answer questions. Essentially, it understands the question, selects a tool, reviews the tool’s result, and then decides whether to answer or try the tool again based on that result.

Final Wikipedia chat assistant application
Final Wikipedia chat assistant application

Project Tasks

1

Get Started

Task 0: Introduction

Task 1: Read in the OpenAI API Key

2

Create the Wikipedia Index

Task 2: Import the Libraries

Task 3: Develop a Script to Index the Wikipedia Pages

Task 4: Create the Documents

Task 5: Creating the Index

3

Build the Chat Agent

Task 6: Import Libraries for the Chat Agent

Task 7: Initialize the Settings Menu

Task 8: Create the Wikipedia Search Engine

Task 9: Create the ReAct Agent

Task 10: Finalize the Settings Menu

Task 11: Script the Chat Interactions

4

Launch the Chat Agent

Task 12: Launch the Chat Agent

Task 13: Use your Chat Agent

Congratulations!