January 25, 2024 • Written By Sherlock Xu
Imagine you are a contestant on a competitive cooking show (like Hell’s Kitchen), required to create a dish that’s not only delicious but also tells a unique story. You already have some cooking skills thanks to your past training experience, but what if you could freely access a global library of recipes, regional cooking techniques, and even flavor combinations? That’s where your sous-chef, equipped with a vast culinary database, steps in. This sous-chef doesn’t just bring you ingredients; she also brings specialized knowledge and inspiration, helping you transforming your cooking into a masterpiece that tells a unique, flavorful story.
This is the essence of Retrieval-Augmented Generation, or RAG in the AI world. Like the sous-chef who elevates your cooking with a wealth of custom resources, RAG enhances the capabilities of large language models (LLMs). It’s not just about responding to queries based on pre-existing knowledge; RAG allows the model to dynamically access and incorporate a vast range of external information, just like tapping into a global culinary database for that unique recipe.
As a partner of LlamaIndex RAG Hackathon, we will release a two-article blog series about RAG to help the BentoML community gain a better understanding of its concepts and usage. In this first post, we will explore the mechanics of this technology, its benefits, as well as the challenges it faces, offering a comprehensive taste of how RAG is redefining the boundaries of AI interactions.
Patrick Lewis and his colleagues at Meta first proposed the concept of RAG in the 2020 paper Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. At its core, RAG has two important components: the retrieval system and the language model.
Traditional language models are like chefs working with a fixed set of ingredients. They can create impressive dishes (responses) based on what they have (their training data), but they are limited to those ingredients. RAG, on the other hand, has the ability to constantly source new ingredients (information), making dishes (responses) far more diverse, accurate, and rich.
In the world of RAG, when a user poses a question, it involves a complex computational process, where embeddings and vectors databases play important roles.
The first step in RAG’s retrieval process involves translating the user’s query into a format that the AI model can understand. This is often done through embeddings or vectors. An embedding is essentially a numeric representation of the query, capturing not just the words, but their context and semantic meaning. Think of it as translating a recipe request into a list of necessary flavor profiles and cooking skills.
Embeddings allow the AI model to process and compare the query against a vast array of stored data efficiently. This process is similar to a chef understanding the essence of a dish and then knowing exactly what ingredients and techniques to use.
After you have the embeddings, the next crucial component is the vector database.
Vector databases in RAG store a massive amount of pre-processed information, each piece also represented as embeddings. When the AI model receives a query, it uses these embeddings to search through the database, looking for matches or closely related information.
The use of vector databases allows RAG to search through and retrieve relevant information with decent speed and precision. It’s like having an instant global connection to different flavors and ingredients, each cataloged not just by name, but by their taste profiles and culinary uses.
Ultimately, the embeddings, the vector database, and the language model work together to make sure the final response is a well-thought-out answer that blends the retrieved information with the AI’s pre-trained data.
RAG comes with a number of benefits. To name a few:
The implications of RAG's benefits extend far beyond just improved answers. They represent an important shift in how we interact with AI, transforming it into a tool capable of providing informed, accurate, and contextually rich interactions. This opens up new possibilities in education, customer service, research, and any other fields where access to updated, relevant information is important.
Key challenges of RAG include:
Despite these challenges, there is great potential of RAG in transforming AI interactions. Its role in enhancing AI’s capabilities is undeniable, and the journey to refine this technology further is both challenging and exciting.
In the next article, we will explore the real-world applications of RAG and its future outlook.