Generative Artificial Intelligence (Gen AI) has graduated from tech jargon to C-suite strategy; a recent McKinsey report indicates that nearly a quarter of top executives are personally using gen AI tools.
Large Language Models (LLMs) are the workhorses behind this evolution, capable of understanding and generating various forms of content. Yet, they're not flawless - issues like generating inaccurate information, inability to verify data sources, and relying on stale data stand out. These challenges are significant for most enterprises as they require real-time, accurate, and traceable data—factors often cited as top concerns.
Retrieval Augmented Generation (RAG) offers a transformative solution to these issues. It elevates the capabilities of LLMs, making them relevant, reliable, and up-to-date. In this article, we'll demystify RAG, delve into its role in augmenting LLMs for better accuracy, and guide you through the working of your own RAG-powered application.
As you read through this article, you'll:
Retrieval Augmented Generation (RAG) is an AI framework that enhances the performance of Large Language Models (LLMs) by integrating them with external knowledge sources. This enables the LLM to generate responses that are not only contextually accurate but also updated with real-time information.
Retrieval-Augmented Generation (RAG) offers an efficient way to adapt and customize Large Language Models (LLMs) without the complexities of fine-tuning or limitations of prompt engineering. Let’s understand how.
In fine-tuning, a pre-trained language model like GPT-3.5 Turbo or Llama-2 is retrained on a smaller, specialized, labeled dataset. While you're not starting from scratch, retraining and deploying a model is expensive, and preparing this new data presents its own set of challenges. Subject-matter experts are needed to label the data correctly. Additionally, the model can start making more errors if the data changes or isn't updated frequently. This makes fine-tuning a resource-intensive and repetitive task. Imagine having to do this every time your company releases a new product, just to ensure your support teams do not receive outdated information from your Gen AI model.
In terms of cost-efficiency, using vector embeddings, a core component of the Retrieval-Augmented Generation (RAG) model, is about 80 times less expensive than using OpenAI's commonly used fine-tuning API. While these terms may seem technical, the real takeaway is efficiency; RAG offers significant advantages in both process complexity and operational cost.
Acknowledging the resource-heavy nature of fine-tuning, it's worth examining the limitations of directly copy-pasting data via prompts to apps such as ChatGPT. Enterprises and LLMs, in particular, face the dual challenges of data privacy and technical constraints. Privacy is a concern for any company that can't afford to expose sensitive documents.
Besides privacy, there's a technical constraint: the “token limit.” This restricts the volume of data you can feed into an LLM for a given query or prompt, causing errors when you exceed this threshold.
The response below on a ChatGPT is a classic example.
RAG LLM adeptly navigates these challenges by retrieving most pertinent information, all within the model's token limit, from a set of data sources.
Examples of these data sources include your existing databases such as PostgreSQL, unstructured PDFs, Rest APIs, or Kafka topics. This targeted retrieval not only enhances the model's accuracy but also maintains the relevance and currency of its responses.
Before delving into how RAG operates, it's vital to familiarize ourselves with two foundational pillars: vector embeddings and vector indexesVector IndexA vector index is an indexing technique applied to data stored in vector form. Th... Read more .
Vector Embeddings serve as numerical fingerprints for various data types, be it words or more intricate "objects." To understand this, picture a 3-D color space, where similar colors are close together. For example, Kelly Green is represented by (76,187,23) and similar colors are placed near to it. This proximity enables easy, objective comparisons.
Source: Huggingface / jphwang / colourful vectors
Like this, vector embeddings group similar data points as vectors in a multi-dimensional space.
OpenAI's text-embedding-ada-002 is commonly used for generating efficient vector embeddings from various types of structured and unstructured data. These are then stored in vector indexes, specialized data structures engineered to ensure rapid and relevant data access using these embeddings.
It's worth noting that applications like recommender systems also use vector indexes and often manage these using vector databases in production settings. However, this isn't required for RAG-based applications. Specialized libraries designed for RAG applications, have the capability to generate and store their own vector indexes within the program memory.
Understanding how Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) requires a walk-through of the underlying processes. The functioning can be broadly categorized into several key components and steps:
While the process may seem intricate at first glance, specialized libraries like Pathway LLM App can greatly simplify it. This library comes with built-in data connectors, generates its own vector indexes, and incorporates an efficient retrieval mechanism based on widely-used algorithms like LSH. For most use cases, the LLM App can be used to productionize LLM Apps without the hassle of using vector databasesVector DatabaseA vector database is a specialized database designed to store, index and query da... Read more . This feature allows for varied deployment scenarios while it serves as a one-stop solution for effortless RAG implementation .
For instance, there's an open-source RAG application that leverages the Amazon Prices API and a CSV file to provide a Streamlit-based chat interface. This interface fetches relevant discount information and communicates it in natural language.
The technology opens up exciting opportunities when paired with various real-time or static data sources, such as internal wikis, CSV files, JSONLines, PDFs, APIs, and streaming platforms like Kafka, Debezium, and Redpanda.
Implementing RAG through such architecture in LLMs offers several advantages:
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