LlamaIndex Tutorial – Build AI Applications with Knowledge Graphs and LLMs

Learn how to use LlamaIndex to connect large language models (LLMs) with your own data. This tutorial covers building knowledge graphs, querying data with LLMs, and integrating with AI applications.

1. Introduction

LlamaIndex (formerly GPT Index) is a framework for connecting your data with large language models (LLMs).

  1. Organizes and indexes structured or unstructured data.
  2. Enables LLMs to answer questions, summarize, and reason over custom datasets.
  3. Works with LangChain, OpenAI, Hugging Face models, and other AI tools.

2. Installation


pip install llama-index
pip install openai

3. Basic Usage

3.1 Creating a Simple Index


from llama_index import SimpleDirectoryReader, GPTSimpleVectorIndex

# Load documents from a folder
documents = SimpleDirectoryReader("data").load_data()

# Create a vector index
index = GPTSimpleVectorIndex(documents)

# Query the index
response = index.query("Summarize the main points from all documents.")
print(response)

3.2 Querying Data with LLM


from llama_index import GPTSimpleVectorIndex, LLMPredictor
from langchain.chat_models import ChatOpenAI

# Initialize LLM predictor
llm_predictor = LLMPredictor(llm=ChatOpenAI(model_name="gpt-4", temperature=0))

# Build index
index = GPTSimpleVectorIndex(documents, llm_predictor=llm_predictor)

# Ask a question
response = index.query("What are the key insights from the uploaded documents?")
print(response)

4. Features

  1. Vector Indexing: Store and query textual data efficiently.
  2. Integration with LLMs: Works with OpenAI, Hugging Face, and LangChain.
  3. Flexible Data Sources: Supports PDFs, text files, and structured datasets.
  4. Knowledge Graphs: Organize and reason over data.
  5. Querying & Summarization: Ask questions and get concise answers from your data.

5. Best Practices

  1. Preprocess your data for better indexing and retrieval.
  2. Use vector indices for large datasets.
  3. Combine LlamaIndex with LangChain for advanced AI workflows.
  4. Fine-tune prompts to improve answer quality.
  5. Monitor token usage if using OpenAI APIs to manage costs.

6. Outcome

After learning LlamaIndex, beginners will be able to:

  1. Connect LLMs to custom datasets.
  2. Build AI-powered applications that query, summarize, and reason over structured and unstructured data.
  3. Integrate LlamaIndex with LangChain and Hugging Face for advanced workflows.
  4. Create scalable knowledge-driven generative AI applications.