llama_docs_bot/docs/getting_started/starter_example.md

Starter Tutorial

Make sure you've followed the [installation](installation.md) steps first.

Here is a starter example for using LlamaIndex.

Download

LlamaIndex examples can be found in the examples folder of the LlamaIndex repository. We first want to download this examples folder. An easy way to do this is to just clone the repo:

$ git clone https://github.com/jerryjliu/llama_index.git

Next, navigate to your newly-cloned repository, and verify the contents:

$ cd llama_index
$ ls
LICENSE                data_requirements.txt  tests/
MANIFEST.in            examples/              pyproject.toml
Makefile               experimental/          requirements.txt
README.md              llama_index/             setup.py

We now want to navigate to the following folder:

$ cd examples/paul_graham_essay

This contains LlamaIndex examples around Paul Graham's essay, "What I Worked On". A comprehensive set of examples are already provided in TestEssay.ipynb. For the purposes of this tutorial, we can focus on a simple example of getting LlamaIndex up and running.

Build and Query Index

Create a new .py file with the following:

from llama_index import VectorStoreIndex, SimpleDirectoryReader

documents = SimpleDirectoryReader('data').load_data()
index = VectorStoreIndex.from_documents(documents)

This builds an index over the documents in the data folder (which in this case just consists of the essay text). We then run the following

query_engine = index.as_query_engine()
response = query_engine.query("What did the author do growing up?")
print(response)

You should get back a response similar to the following: The author wrote short stories and tried to program on an IBM 1401.

Viewing Queries and Events Using Logging

In a Jupyter notebook, you can view info and/or debugging logging using the following snippet:

import logging
import sys

logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

You can set the level to DEBUG for verbose output, or use level=logging.INFO for less.

Saving and Loading

By default, data is stored in-memory. To persist to disk (under ./storage):

index.storage_context.persist()

To reload from disk:

from llama_index import StorageContext, load_index_from_storage

# rebuild storage context
storage_context = StorageContext.from_defaults(persist_dir="./storage")
# load index
index = load_index_from_storage(storage_context)

* learn more about the [high-level concepts](/getting_started/concepts.md).
* tell me how to [customize things](/getting_started/customization.rst).
* curious about a specific module? check out the guides 👈
* have a use case in mind? check out the [end-to-end tutorials](/end_to_end_tutorials/use_cases.md)