8/26/2024

Pip Install LlamaIndex: Getting Started Guide

Introduction to LlamaIndex

Are you ready to jump into the exciting world of LlamaIndex? 🦙 This framework is designed to help you harness the power of large language models (LLMs) while easily managing and querying your data. Whether you're a developer, a data scientist, or just a curious mind, this guide will get you set up and ready to rock!

LlamaIndex allows for context-augmented AI applications, streamlining the way you work with various data sources. So, without further ado, let's dive into installing LlamaIndex!

Why Use LlamaIndex?

Before we begin, let’s quickly recap why LlamaIndex is the go-to choice for those looking to work with LLMs:

Integration with various data sources: Easily work with APIs, PDFs, SQL, and more!
Flexible data management: Indexing your data allows for quick access and efficient querying.
User-friendly for ALL skill levels: Whether you're just starting or have been in the game for years, LlamaIndex's interface allows you to hit the ground running.

Getting Started with Installation

To get started with LlamaIndex, you'll primarily be using

pip

, the package installer for Python. Below is a step-by-step guide on how to install LlamaIndex on your system to start building your applications.

Step 1: Ensure Python is Installed

Before you can install LlamaIndex, you need to make sure Python is installed on your computer. LlamaIndex supports Python versions >= 3.8.1 and < 4.0. To check your version, run:

1
python --version

If you don't have Python installed, head over to Python's official website to download and install the latest version compatible with your OS!

Step 2: Install Pip (if not already installed)

Most Python installations come with

pip

, but if you need to install it separately, follow the instructions on the pip installation page.

Step 3: Install LlamaIndex Using Pip

To install LlamaIndex, simply open your terminal or command prompt and type:

1
2

bash
pip install llama-index

This command downloads the starter bundle, which includes important packages like:

1llama-index-core
1llama-index-legacy
1llama-index-llms-openai
1llama-index-embeddings-openai
1llama-index-program-openai
1llama-index-question-gen-openai
1llama-index-agent-openai
1llama-index-readers-file
1llama-index-multi-modal-llms-openai

Important Notes

LlamaIndex may need to download and store local files, including libraries like NLTK and HuggingFace. If you'd like to control where these files are saved, you can specify the environment variable
1LLAMA_INDEX_CACHE_DIR
.
Setting up OpenAI Environment: By default, LlamaIndex utilizes OpenAI's
1gpt-3.5-turbo
for text generation and
1text-embedding-ada-002
for embeddings. Make sure to set your
1OPENAI_API_KEY
as an environment variable. You can obtain your API key by logging into your OpenAI account and generating a new key.
- For MacOS/Linux, use:
  1 2bash export OPENAI_API_KEY=your_api_key_here
- For Windows, use:
  1 2cmd set OPENAI_API_KEY=your_api_key_here

Custom Installations

If you don't wish to use OpenAI or would like to install LlamaIndex selectively, you can do so by installing individual packages. For example, if you're setting up a local environment with HuggingFace embeddings, your installation command would look like this:

1
pip install llama-index-core llama-index-readers-file llama-index-llms-huggingface

This custom approach allows for greater flexibility depending on your project requirements.

Installation from Source

If you're feeling adventurous and wish to make some direct changes or simply want the latest version, you might prefer installing LlamaIndex from the source:

First, clone the repository:
1 2bash git clone https://github.com/run-llama/llama_index.git
Next, you need to set up Poetry, a tool to manage dependencies. Install it following the instructions on the Poetry website.
Create a virtual environment to ensure your packages are contained:
1 2bash poetry shell
Finally, install the required packages by running:
1 2bash poetry install
If you want local development dependencies, consider:
1 2bash poetry install --with dev, docs

Ready to Go!

Congrats! 🎉 You have LlamaIndex installed. Now you can begin exploring its functionalities!

Getting Your Hands Dirty

Example Usage for Beginners

Now that you have installed LlamaIndex, let’s walk through a simple example to help you get started:

Setup your data source: LlamaIndex works best with structured data. Let’s grab a text from Paul Graham's essay to use as our dataset. Store the text in a folder named
1data
.
Create a Python script: Create a file called
1starter.py
with the following code: ```python from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
documents = SimpleDirectoryReader("data").load_data() index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine() response = query_engine.query("What did the author work on growing up?") print(response) ```
Run Your Script!: Now, execute your script using:
1 2bash python starter.py
You should receive a response based on your query! Sweet, right?

Logging and Persistence

Wanna view what's under the hood? You can add logging to your script by including:

1
2
3

python
import logging
logging.basicConfig(level=logging.DEBUG)

And for persisting your index so you don't have to reload it every time, use:

1
2

python
index.storage_context.persist(persist_dir="./storage")

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Final Thoughts

There you have it, folks! You are now ready to use LlamaIndex and start building context-augmented applications that can significantly enhance your data processing workflows. If you run into any issues or have questions, the community is here to help, so don’t hesitate to engage and learn together! Happy coding! 🚀