What is RAG?

Building a RAG System: Architecture and Future Innovations

During my time at Cloud303, I designed a comprehensive Retrieval-Augmented Generation (RAG) system that pushed the boundaries of what was possible with Generative-AI. For those unfamiliar, RAG systems are built to combine the power of Large Language Models (LLMs) with relevant, up-to-date data, creating smarter, more context-aware AI applications. In this post, I’ll break down the architecture of a RAG system, some standards methods for implementing RAG, and some new methods coming down the pipeline, like cRAG and Graph-based RAGs.

RAG System Architecture: The Core Components
At Cloud303, the RAG system we built was designed to deliver intelligent, dynamic responses that weren’t just static outputs from a language model but infused with live, relevant data. Here's a look at how we structured it:

1. Embedding Model via Bedrock
   The foundation of any RAG system is the ability to convert text into a meaningful vector representation. For this, we used Amazon Bedrock to manage the embedding model. This model was responsible for taking the input text, converting it into embeddings, and allowing for vector-based similarity searches. Bedrock’s managed service made it easy to deploy, scale, and keep everything efficient.

2. Pinecone as the Vector Database
   Once we had the embeddings, we needed a place to store and search through them efficiently. We used Pinecone as our vector database, which allowed to store the vector representations of all our documents and retrieve the most relevant ones based on the user’s query. Pinecone made it seamless to scale and maintain the high availability of our data.

3. Inference Using Claude 3.5 Sonnet
   For the inference layer, we chose to work with Claude 3.5 sonnet. Claude added the generative power to the system by taking the results from Pinecone’s vector search and generating a detailed, context-rich response. By leveraging this LLM, we ensured that our responses were both relevant and coherent, blending information from the query and the retrieved data.

4. Lambda as the API Orchestrator
   AWS Lambda played a critical role in orchestrating the entire RAG pipeline. It acted as the API layer, tying together the embedding model, Pinecone, and Claude 3.5. Every time a query came in, Lambda would handle the workflow—embedding the query, retrieving relevant vectors from Pinecone, and feeding that context into Claude 3.5 to generate the final response. This serverless architecture allowed us to scale without worrying about the underlying infrastructure.

5. A Best Practices RAG Pipeline
   A critical part of any RAG system is keeping the data up to date. In our implementation, we built a continuous update pipeline to ensure new data was regularly embedded and indexed in Pinecone. This pipeline not only updated the vector database but also retrained parts of the model as necessary, making sure that the system evolved as the data did. This kept our system highly relevant and prevented the model from getting stale over time.

Challenges and Key Considerations
Building this RAG system came with its own set of challenges. One major consideration was balancing the real-time nature of information retrieval with the performance overhead. While the system worked efficiently, large-scale data updates needed to be handled without disrupting the current query pipeline. Another challenge was fine-tuning Claude 3.5 to work optimally with the retrieved context while avoiding hallucinations, a common issue with generative models.

New Frontiers: cRAG and Graph-Based RAG
The field of RAG is evolving quickly, with new methods like cRAG (combined RAG) and graph-based RAG gaining traction. These approaches take the current RAG architecture to the next level.

1. cRAG
   cRAG (combined Retrieval-Augmented Generation) combines the traditional RAG setup with more advanced data structures and knowledge bases, allowing for deeper connections between the retrieved documents. This method improves context aggregation, leading to more accurate and relevant responses. As outlined in the LangChaindocumentation, cRAG optimizes how we combine information, increasing the system's ability to draw from multiple sources of truth in a structured way.

2. Graph-Based RAG
   Another emerging innovation is the use of graph databases in RAG systems. Instead of relying solely on vector searches, graph-based RAGs leverage knowledge graphs to provide richer, more interconnected data retrieval. This method allows AI systems to understand and query complex relationships between pieces of information, which could be game-changing for industries like healthcare, finance, and law. By using graph structures, we can ensure that the system not only retrieves relevant documents but also understands the relationships between them, resulting in a much smarter and more nuanced response.

Looking Ahead
As the technology behind RAG continues to evolve, we’re going to see more practical applications far beyond chatbots. The future of AI involves AI agents that can leverage RAG systems to perform tasks autonomously, and innovations like cRAG and graph-based retrieval will only accelerate that progress.

The next big step is moving toward an ecosystem where AI systems can retrieve, reason, and respond autonomously, using a combination of vector-based retrieval, graph theory, and generative models. With infrastructure improving and models becoming more powerful, we’re closer than ever to seeing this future become a reality.

"Dream big, deliver bigger"

Sujaiy Shivakumar

Sujaiy Shivakumar is a 3x tech startup founder, with his latest successful venture, Cloud303, growing into an AWS Premier Partner with over 60 employees. All views and opinions expressed in this blog are entirely his own.

LET'S CONNECT

Relationships shape the future. I'm always open to connect and collaborate.

FOLLOW ME ON LINKEDIN: