Build a RAG Chatbot Using DeepSeek and LlamaIndex

Traditional chatbots rely solely on pre-trained knowledge, often providing outdated answers. retrieval-augmented generation (RAG) solves this by combining large language models with real-time information retrieval, allowing AI assistants to fetch relevant context before generating responses. This powers modern question-answering systems that need accurate, up-to-date information from external sources.

In this project, we'll build a RAG chatbot using DeepSeek, LlamaIndex, and Chainlit that answers questions by retrieving content from Wikipedia. Users select their LLM model (LLaMA 3 or DeepSeek) through a Chainlit interface and specify topics to index. We'll use the Groq API for LLM inference, LlamaIndex for document chunking and vector indexing, and implement a ReAct agent framework that enables step-by-step reasoning and tool usage based on retrieved context.

We'll configure API keys, build a Wikipedia indexing script that creates a vector index for semantic search, develop the chat agent with a settings menu, and integrate the ReAct agent for agentic reasoning. By the end, you'll have a working RAG application demonstrating LlamaIndex integration, DeepSeek API usage, Chainlit development, retrieval-augmented generation, and agentic workflows applicable to any conversational AI system.