Getting Started with LlamaIndex | Deepchecks

Getting Started with LlamaIndex

Brain John Aboze

May 27, 2024 | 13 mins

This blog post was written by Brain John Aboze as part of the Deepchecks Community Blog. If you would like to contribute your own blog post, feel free to reach out to us via blog@deepchecks.com. We typically pay a symbolic fee for content that's accepted by our reviewers.

Introduction

Are you tired of language models’ generic answers? Imagine if they could learn directly from your notes, documents, or even your favorite websites. That’s the power of LLamaindex. This innovative framework lets you connect your data to cutting-edge large language Models (LLMs), transforming them into ultra-smart tools tailored to your needs. With LLamaindex, those LLMs can answer complex questions based on your information, generate summaries aligned with your interests, and even become engaging conversational AI assistants that truly understand your world.

Initially known as the GPT Index, Llamaindex is a powerful data framework designed to enhance applications powered by LLMs. LLamaindex provides the tools to easily ingest, structure, and access your private or specialized data. This allows LLMs to use this data safely to generate more informed and accurate text. It’s available in both Python and Typescript. Think of LLamaindex as a bridge between your custom data and powerful LLMs like GPT-4. Whether your information lives in APIs, databases, or PDFs, LLamaindex organizes it for smooth interaction with these intelligent language models. This bridge greatly enriches the data LLMs access, enabling them to create highly tailored applications and workflows. It acts as a multi-faceted orchestrator when working with data and LLMs:

Power of Context Augmentation

LLMs are trained on massive amounts of public information; they often can’t access domain-specific information, the latest updates, your company’s internal documents, or a specialized industry database.

Fine-tuning an LLM with your data is one option, but it has drawbacks:

Retrieval Augmented Generation (RAG) offers a smarter alternative. Here’s how it works:

While fine-tuning LLMs can be useful, it has limitations. RAG offers a compelling solution, addressing these concerns:

Ultimately, the best approach depends on your application’s specific needs and the resources you have available. Consider the trade-offs between flexibility, cost, and customization when making your decision.

RAG Overview

You might wonder, “Why bother with RAG? Can’t I just dump all my data into the LLM and let it sort things out?” It’s a good question, but here’s the catch: LLMs have limits. Think of an LLM’s understanding as reading a book where you can only see a few lines at a time. That’s the context window. Every time you move forward, you forget what came before. This makes it hard for the LLM to grasp the bigger picture. RAG shines by focusing your LLM’s attention. Instead of overwhelming it with everything, RAG works like a spotlight:

Hence, this makes the systems more cost-effective and improves answers. Let’s look at what a RAG workflow looks like in detail.

The workflow includes the following processes:

Key Stages of Building RAG Applications

We will delve into the broader processing of building LLM-powered applications fueled by your data using LlamaIndex. RAG is a powerful core concept; let’s get started.

Installation and setup

Before we discuss all the cool things LlamaIndex can do, let’s set it up on your system.

For the tutorial, we will be utilizing the Python version of Llamaindex.

Step 1: Create a Virtual Environment (Mac/Linux and Windows)

Virtual environments keep project dependencies separate, avoiding conflicts. Here’s how to create one (instructions will vary slightly based on your setup):

Step 2: Install LlamaIndex

Inside your activated virtual environment, run this command: ​​pip install llama-index openai

Step 3: Set Up Your OpenAI API Key

Security Reminder: Never share your OpenAI API key publicly. It gives access to your OpenAI account.

Loading data

Llamaindex provides data connectors that ingest information from all your sources-APIs, databases, PDFs, and more. You can view a comprehensive list of all the data loaders available on Llama Hub. For this article, we will ingest a PDF file, so we will utilize the SimpleDirectoryReader, one of the most used data connectors.

Let’s install PyPDF2, a powerful tool for working with PDFs. It allows us to easily split, merge, crop, and modify PDF files. Here’s how to install it: pip install pypdf

Next, let’s fetch a PDF online (Here, it’s an Nvidia PDF on their story) and save it in our local storage. I am using the same directory as my notebook.

This will create a local directory and save the PDF locally. Next, we will load the PDF into a LlamaIndex document object.

The LlamaIndex document object is a core abstraction within LlamaIndex. It acts as a container for a given data source, whether you create it manually or use one of LlamaIndex’s automatic data loaders. It has some attributes, such as metadata, which stores additional information about the document, and relationships, which help track how it connects to other documents.

Chunking (Node Parsing)

The next step is to parse this document object into chunks (or nodes) and eventually turn each node into an embedding, which is just a numeric representation of the meaning of that chunk of text. Transformer models (that large language model often used) work with a specific amount of text at a time, which is the context window we discussed earlier. Even if the LLM context is large enough to fit the entire text, a single numeric representation (vector) trying to summarize pages loses important details. It’s like describing a whole movie based on a single blurry snapshot. The ideal is for each chunk of text fed to the model to have a complete meaning – a sentence or paragraph makes sense independently.

Splitting your documents into meaningful chunks (like sentences or paragraphs) helps the transformer model focus on each part individually, leading to better understanding and more accurate results. Chunks that are too short might not have enough information for the model to work with. Chunks that are too big go back to the problem of losing detail within the model’s limited attention window. The perfect chunk size might depend on your specific model and the kind of text you’re working with.

Let’s explore the range of text node parsers offered by LlamaIndex, from basic options to more advanced parsers.

With all the different node parsers defined, we can get the respective node objects as follows:

These node types vary in size and carry important information. Each TextNode of a given node type has a unique ID and also contains .metadata and .relationship properties. These provide additional information about the node itself and its connections to other nodes, respectively, enhancing how the language model can understand and process your data.

Indexing

After you’ve loaded your data, LlamaIndex structures it for effortless retrieval. It does this using an Index, a specialized data structure designed for efficient searches by language models. LlamaIndex offers several index types, including:

Storing Indexing

While these indices make querying your data fast and easy, they must be recreated with each session. This can become computationally expensive, especially when working with large amounts of text. By persisting them to some form of memory, you avoid the need to recreate them each time, saving significant computational resources and only having to add to it with the increasing knowledge base.

Persisting index to local directory

Let’s just take one of the indices to understand this process better, so let’s say we want to persist the setence_window_nodes_indices.

This yields a local_storage folder with the following file structure:

. ├── default__vector_store.json ├── docstore.json ├── graph_store.json ├── image__vector_store.json └── index_store.json

This leads to the creation of JSON files for persistent storage. However, two files are particularly important for us: docstore.json and default__vector_store.json. The docstore.json file contains the node IDs with respective metadata and reference document information, while the default__vector_store.json contains all the embeddings.

LLM Application

Data-backed LLM applications streamline the way you interact with your knowledge. Instead of sifting through files or databases, you get direct answers to questions, engage in informative chats, or even delegate tasks to adaptive decision-making systems.

In our use case, we need to make queries on the knowledge base; hence, we will create a query engine from our retrieved indices.

Conclusion

Throughout this guide, we’ve explored how LlamaIndex unlocks the hidden potential of your data. By structuring it intelligently, LLMs can move beyond generic responses and truly understand your world. We’ve covered:

Think of LlamaIndex as the bridge between your knowledge and the incredible power of large language models. Whether you want to supercharge your search, build an insightful chatbot, or create a problem-solving AI assistant, the path starts here. This is just the beginning – as LLM technology and tools like LlamaIndex evolve, the possibilities are endless.