Rag Architecture Easy Explained

Introduction

Hi, I'm Akesh Kumar, and today I’ll break down the architecture of a Retrieval-Augmented Generation (RAG) model.
RAG addresses a critical issue by blending the strengths of information retrieval systems and generative models. This fusion creates a solution that's both accurate and dynamic, perfect for answering complex queries.

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First, let's look at the problem in Traditional models.
Systems like search engines and recommendation systems retrieve information based on algorithms, such as similarity matching or Levenshtein distance. While they may return millions of results, the user still has to sift through numerous articles and pages to find the exact information they need.

On the other side, Large Language Models (LLMs) are trained on massive datasets but rely on static information. This limits their ability to incorporate recent knowledge unless they're retrained, which is both time-consuming and costly. Worse, they can 'hallucinate' or forget previous knowledge when fine-tuned for specific tasks.

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Trade-off in LLMs

"There's an inherent trade-off in LLMs between memorization and generalization."

If the model focuses too much on memorizing specific data, it loses flexibility and risks overfitting. When this happens, the model struggles to handle new, unseen information.

On the flip side, if it generalizes too much, it might not remember precise details, which can lead to vague or even inaccurate responses. Worse yet, it may hallucinate, generating content that wasn't part of its original training.

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How RAG Solves This Problem

"This is where RAG steps in."

RAG (Retrieval-Augmented Generation) combines both retrieval and generation. Rather than relying solely on a model’s internal knowledge, it retrieves the latest, most relevant information from external sources in real time, like databases, websites, or even PDFs.

With this approach, the model can provide accurate and up-to-date information without needing to be retrained. By combining this retrieval capability with a language model, RAG delivers responses that are both accurate and contextually relevant.

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Architecture Overview

"Now, let’s break down the RAG architecture step-by-step."

1. User Input

   • It all starts with user input, which could be a URL, a query, or even a document like a PDF.

2. Retrieval Phase

   • The input is transformed into a vector using an embedding model—like BAAI/bge-base-en-v1.5—which represents the query in a numerical format.
   • This vector is passed to a retrieval system, such as ChromaDB, which searches for the most relevant data from large databases or document collections.

3. Document/Chunk Retrieval

   • Next, the relevant documents or chunks of text are retrieved to ensure the model is using up-to-date and factual information.

4. Language Generation

   • The retrieved information is then passed to a language model—like Google Gemini API—to generate a well-formed response by blending retrieved data with the model’s generative abilities.

5. Response Output

   • Finally, the response is presented to the user, whether it's an answer to a query or a summarized document.

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Key Advantages

By keeping the retrieval and generation processes separate, RAG maximizes both. It retrieves relevant, up-to-date data in real-time and uses that to generate accurate, contextually appropriate responses. This solves the memorization vs. generalization trade-off inherent in traditional LLMs.

Another major advantage is scalability. You can update the external knowledge sources as often as needed without having to retrain the model.

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Conclusion

In summary, RAG brings the best of both worlds by integrating real-time retrieval and generative models. This solves the key limitations of traditional search engines and static LLMs, offering an efficient and scalable solution for intelligent, up-to-date responses.

Project and Example of RAG: URL

"Thanks for reading! Feel free to reach out if you want to learn more about RAG or have any questions."