AWS Partner Network (APN) Blog
Building Serverless RAG Applications with Couchbase and Amazon Bedrock
By Amir Tarek, Senior Cloud Operations Architect – AWS
By Mohamed Salah, Senior Solutions Architect – AWS
By Yves Laurent, Director Technology Partnerships – Couchbase
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| Couchbase |
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In today’s Artificial Intelligence (AI) landscape, organizations can leverage their unique data as a key differentiator. While foundation models (FMs) provide powerful capabilities, combining them with industry-specific information creates more precise, relevant, and trustworthy responses for specific use cases.
This post explores how Couchbase vector search capabilities combined with Amazon Bedrock foundation models create a powerful, Serverless Retrieval Augmented Generation (RAG) solution. This blog post will demonstrate how this integration enables you to build intelligent applications that understand and respond to user queries with accuracy while maintaining cost efficiency through a Serverless architecture.
The Challenge: Connecting Foundation Models with Enterprise Data
Organizations implementing AI face a fundamental challenge: how to effectively combine their proprietary data with foundation models while maintaining security and operational efficiency. Generic FM responses often lack context from an organization’s specific data, and FMs are trained on historical data without access to the latest information. Traditional AI architectures can be expensive to maintain and scale, and managing the components needed for effective AI applications requires significant expertise.
The combination of Couchbase and Amazon Bedrock addresses these challenges through Serverless RAG architecture.
This solution enables real-time connections between enterprise data and foundation models while automatically scaling to meet demand. By leveraging Couchbase’s event-driven capabilities and AWS managed services, organizations can enhance every AI interaction with their specialized knowledge while reducing operational overhead.
Solution Overview: Serverless RAG with Couchbase and AWS Bedrock
Our solution leverages several key components:
- Couchbase Capella: A fully managed database service that provides vector search capabilities for efficient similarity matching and document retrieval
- Amazon Bedrock: A fully managed service offering foundation models from leading AI companies through a unified API
- AWS Lambda: Serverless compute that processes data and handles requests without infrastructure management
- Amazon API Gateway: A fully managed service for creating, publishing, and securing APIs
The architecture follows a Serverless approach, making sure you only pay for what you use while maintaining high performance and scalability.
Architecture Deep Dive
Figure 1 – RAG Applications with Couchbase and Amazon Bedrock
When users interact with the system, their questions trigger a workflow optimized for fast, relevant responses. The query travels through API Gateway to Lambda, which coordinates the RAG process.
First, the question is converted into a vector embedding using Amazon Bedrock. This embedding is used to perform a similarity search in Couchbase’s vector store, identifying the most relevant information from the organization’s data. The Couchbase Eventing Service makes sure that new or updated data is automatically processed and vectorized in real-time, maintaining the freshness of the vector store without additional latency.
The retrieved context, along with the original question, is sent to the Amazon Bedrock foundation models.
The FM then generates a response that incorporates both its pre-trained knowledge and the specific context from the organization’s data. This response is returned to the user, providing information that is both accurate and contextually relevant to the organization.
Key Components in Detail
Couchbase Vector Search
Couchbase vector search capabilities provide the foundation for efficient semantic retrieval. By indexing vector embeddings alongside traditional data, Couchbase enables hybrid search approaches that combine the best of semantic understanding and keyword precision.
The vector search functionality is fully integrated with Couchbase’s database capabilities, allowing organizations to maintain a single data solution for both operational and AI workloads. This integration simplifies the architecture and reduces the need for data movement between systems.
Couchbase Eventing Service
The Eventing Service transforms the architecture from batch-oriented to real-time by automatically triggering workflows as soon as a new source data is added or changed in Couchbase. This capability makes sure that the vector index remains current as new information enters the system.
By processing data asynchronously and at scale, the Eventing Service maintains system performance while making sure that information is promptly available for AI-powered retrieval.
Amazon Bedrock
Amazon Bedrock provides the AI foundation for the solution through its comprehensive selection of foundation models. These models power two critical functions: generating vector embeddings that capture semantic meaning and producing contextually relevant responses based on retrieved information.
The unified API simplifies integration, while enterprise-grade security features make sure that sensitive data remains protected throughout the AI workflow. With Amazon Bedrock, none of the customer’s data is used to train the original base models, and data is encrypted in transit and at rest. This facilitates robust protection for sensitive information throughout the AI workflow.
Serverless Components
The Serverless components of the architecture Lambda and API Gateway work together to create a system that scales automatically with demand. This approach minimizes the need for capacity planning and infrastructure management, reducing both operational complexity and cost.
By paying only for the resources used during actual processing, organizations can implement AI capabilities without the significant upfront investment traditionally associated with such systems.
Benefits and Use Cases
This Serverless RAG architecture with Couchbase and Amazon Bedrock delivers several key enterprise benefits. Resource optimization stands at the forefront, allowing organizations to scale automatically based on demand while only paying for consumed resources. This alleviates the burden of managing and paying for idle infrastructure, bringing significant operational efficiency to AI implementations.
Response quality sees substantial improvement through this architecture. By augmenting FM outputs with enterprise-specific context, organizations can maintain data freshness and deliver highly relevant responses to users. The system continuously incorporates current information, making sure that AI interactions remain accurate and valuable over time.
Development teams benefit from a streamlined implementation process through fully managed AWS services. This approach reduces infrastructure management overhead and accelerates time to production, allowing teams to focus on creating value rather than managing infrastructure. The architecture also provides robust enterprise security features, maintaining full control over sensitive data while leveraging the comprehensive security capabilities of AWS.
Organizations are implementing this architecture across various use cases. In document management, teams are building intelligent systems that enable natural language querying of large repositories, making information discovery more intuitive and efficient. Knowledge management solutions benefit from self-improving bases that understand user intent and automatically organize content for optimal retrieval.
Customer support teams are implementing AI-enhanced assistance systems that maintain real-time access to company information, facilitating consistent and accurate responses to customer inquiries. Research teams are using this architecture to accelerate their work, enabling efficient exploration of large datasets and uncovering valuable patterns and insights that might otherwise remain hidden.
Conclusion
The combination of Couchbase vector search capabilities and Amazon Bedrock foundation models creates a powerful, Serverless RAG solution that connects AI with enterprise data. By leveraging Serverless architecture, organizations can build intelligent applications that deliver contextually relevant responses while maintaining cost efficiency and operational simplicity.
This approach enables you to customize FM responses with your proprietary data without the need for costly model fine-tuning or complex infrastructure management. As AI continues to transform business operations, solutions like this will be essential for organizations looking to leverage the power of foundation models while maintaining control over their data and costs.
You are encouraged to explore this architecture and adapt it to your specific use cases. The Serverless approach provides flexibility and scalability that can grow with your needs, making it an ideal foundation for your AI journey.
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Couchbase – AWS Partner Spotlight
Couchbase empowers developers and architects to build, deploy, and run their most mission-critical applications. Couchbase delivers a high-performance, flexible and scalable modern database that runs across the data center and any cloud. Many of the world’s largest enterprises rely on Couchbase to power the core applications their businesses depend on.