Build a "Chat with Your Docs" Bot Using RAG and LlamaIndex

Create an intelligent bot that can answer questions based on your own documentation using Retrieval-Augmented Generation (RAG) and LlamaIndex.

Posted on: 2026-03-16 by AI Assistant

Build a “Chat with Your Docs” Bot Using RAG and LlamaIndex

Have you ever spent hours searching through dense technical documentation just to find a single configuration flag? What if you could simply ask your documentation a question and get a precise, context-aware answer instantly?

In this tutorial, you will learn how to build a “Chat with Your Docs” bot using Retrieval-Augmented Generation (RAG) and the LlamaIndex framework.

Prerequisites

Python 3.10 or higher.
An OpenAI API Key (or a local model setup).
A folder containing some Markdown or text documents you want to query.

What is RAG?

Retrieval-Augmented Generation (RAG) is a technique that grounds Large Language Models in your specific data. Instead of relying solely on the model’s pre-trained knowledge, RAG searches your documents for relevant information and provides that context to the model before it generates an answer.

Setting Up LlamaIndex

First, install LlamaIndex and its dependencies:

pip install llama-index

Building the Index

Create a folder named data and place some sample documentation files (e.g., Markdown files) inside it.

Next, create a script named build_bot.py:

import os
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

# Set your API key
os.environ["OPENAI_API_KEY"] = "your-api-key-here"

def build_and_query():
    print("Loading documents...")
    # 1. Load data from the directory
    documents = SimpleDirectoryReader('data').load_data()
    
    print("Building the index...")
    # 2. Create an index over the documents
    index = VectorStoreIndex.from_documents(documents)
    
    # 3. Create a query engine
    query_engine = index.as_query_engine()
    
    # 4. Ask a question!
    question = "How do I configure the main database connection?"
    print(f"\nQuestion: {question}")
    
    response = query_engine.query(question)
    print(f"Answer: {response}")

if __name__ == "__main__":
    build_and_query()

How It Works

SimpleDirectoryReader: Reads all the files in your data folder and extracts the text.
VectorStoreIndex: Converts the text into vector embeddings and stores them in an in-memory vector database.
Query Engine: Takes a natural language query, searches the index for the most relevant text chunks, and passes them to the LLM to synthesize a final answer.

Conclusion & Next Steps

You’ve built a functional RAG pipeline in under 30 lines of code! You can now query your own documents naturally.

For your next step, try replacing the in-memory index with a persistent vector database like ChromaDB or Pinecone so you don’t have to rebuild the index every time you run the script. You can also explore adding a chat UI using Streamlit or Gradio.

rag llamaindex documentation