Build an LLM-powered Chatbot with RAG using LlamaIndex

In this project, we'll build an intelligent conversational AI chatbot using retrieval-augmented generation (RAG) to enhance large language model (LLM) responses with factual Wikipedia content. Unlike standard chatbots that rely solely on pre-trained knowledge, our RAG-powered agent will retrieve real-time information from Wikipedia pages, ground its answers in verified sources, and provide accurate, context-aware responses. We'll use LlamaIndex for document indexing and retrieval, OpenAI's GPT models for natural language generation, and Chainlit for the interactive chat interface.

We'll start by setting up OpenAI API authentication and importing necessary libraries including LlamaIndex and Pydantic for data validation. Next, we'll develop a Wikipedia indexing script that loads Wikipedia pages, chunks the content into searchable documents, and creates a vector index for efficient semantic search. Then we'll build the ReAct agent framework, which enables step-by-step reasoning where the agent analyzes questions, selects appropriate tools, reviews results, and iteratively refines its approach until reaching an answer. We'll initialize the Chainlit chat interface, configure the Wikipedia search engine as a retrievable tool, and script the conversation flow.

Finally, we'll launch the chatbot application and test it with real queries, demonstrating how retrieval-augmented generation improves response accuracy by grounding LLM outputs in factual knowledge bases. By the end, we'll have a production-ready RAG chatbot showcasing LlamaIndex vector indexing, OpenAI integration, conversational AI design, and knowledge retrieval techniques applicable to any question-answering system or AI assistant requiring factual accuracy.