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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).
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
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.
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!