Demonstrating the Use of Retrieval Augmented Generation RAG

Introduction

Welcome to this informative session on Retrieval Augmented Generation (RAG), hosted by Tom Kanum, the director of the AI Lab at the Center for Applied AI. The goal of today’s discussion is to provide an overview of RAG and demonstrate its application in real-world scenarios. Today, AI Engineers Devin Saloner and Sidhant Gupta will guide us through the intricacies of RAG and its utility in enhancing the capabilities of large language models (LLMs).

Overview of RAG: What is it?

RAG, or Retrieval Augmented Generation, is a method aimed at improving the functionality of LLMs by enabling them to leverage external information dynamically. Unlike traditional models, RAG allows these systems to retrieve relevant data from external databases in real time. This provides several advantages:

Up-to-Date Information: RAG helps models stay current by allowing new information to be inserted into databases without the need for retraining. This is especially useful as it saves time and computational resources.
Enhanced Relevance and Trust: By pulling real-time data, RAG can produce responses that are not only accurate but also contextually rich. It reduces instances of misinformation or hallucinations as the model can cite its sources.

RAG consists of three core components:

Large Language Model (LLM): For example, models like ChatGPT or GPT-4.
Embedding Model: This converts queries into vectors or numerical representations, which can then be stored in a vector database.
Vector Database: Think of this as a spatial representation of embeddings, allowing for efficient data retrieval based on similarity searches.

How RAG Works

The process involves the following steps:

A user query is sent to the embedding model.
The model converts the query into a vector embedding and performs a similarity search in the vector database.
Relevant data is retrieved based on the closest match to the query.
The final response is generated by the language model using the retrieved information.

The AI Lab conducted benchmarks to evaluate various embedding models and vector databases. Different metrics such as "embeds per second," "response time," and "correctness" were analyzed, leading to recommendations for optimal tools within these categories.

Live Demonstration of RAG Application

Sidhant Gupta then provided a live demonstration using OpenAI's APIs, specifically with the GPT-4 mini model and Chromadb as the vector database. In this demo, Sidhant showed the stark differences in response when using RAG compared to standard queries without external data retrieval.

He used personal data (his resume) as a source document to illustrate the significance of RAG. Without RAG, the model simply stated that it could not access the personal document. However, with RAG in action, it provided specific details about Sidhant's education and work experience, showcasing the model’s capacity to offer personalized and relevant responses.

This demonstrates how RAG can be a turning point for businesses and individuals, especially in improving automated interactions and information retrieval.

Conclusion

The session wrapped up by emphasizing the increasing importance of RAG in various applications across industries, from academia to business. The integration of RAG into AI systems allows for the retrieval of personalized information and enhances overall user experience.

Thank you for joining our discussion today and we look forward to seeing how RAG could be utilized in your projects.

Keywords

Retrieval Augmented Generation (RAG)
Large Language Models (LLMs)
Vector Database
Embedding Model
Real-time Data Retrieval
Accuracy
Contextual Relevance

FAQ

Q1: What is Retrieval Augmented Generation (RAG)?
A: RAG is a method that enhances large language models by allowing them to retrieve relevant external information in real time for generating more accurate and contextually relevant responses.

Q2: How does RAG improve the performance of language models?
A: RAG enables models to access the latest information without retraining, reduces hallucinations by providing reliable sources, and saves time and resources compared to traditional training methods.

Q3: What components are involved in a RAG system?
A: A RAG system typically involves a large language model, an embedding model, and a vector database.

Q4: How does the retrieval process work in RAG?
A: The user query is converted into a vector embedding, searched in the vector database for relevant information, and the results are then utilized by the language model to generate responses.

Q5: Can you give an example of RAG in action?
A: In the demonstration, a user queried their education and work experience, and the model provided accurate information after retrieving data from a resume document, showing the effectiveness of RAG in personalizing responses.