Enhancing AI’s Potential with Retrieval Augmented Generation (RAG) in Multi-Tenant Environments
Overview of RAG and Its Importance in AI
The Role of RAG in SaaS: Personalized AI Experiences
Technical Challenges in Utilizing Tenant-Specific Data
Implementing RAG with Amazon Bedrock Knowledge Bases
Tenant Isolation Patterns: JWT and FGAC
Exploring the Advantages of JWT for Multi-Tenant Environments
Solution Overview: Architecture and Implementation
Prerequisites for Implementing RAG with OpenSearch
User Authentication and JWT Generation Process
Routing Requests to Tenant Data
Data Isolation Strategies in OpenSearch Service
Considerations for a Robust Implementation
Conclusion: A Practical Approach to Multi-Tenant RAG Solutions
Augmenting Language Models with Retrieval-Augmented Generation (RAG): A Multi-Tenant Approach
In recent years, large language models (LLMs) have significantly advanced artificial intelligence (AI) across various sectors. However, to truly leverage their capabilities, there is a growing necessity for integration with external data sources. This is where Retrieval-Augmented Generation (RAG) comes into play, standing out as an effective strategy to enhance the performance and accuracy of LLMs.
What is RAG?
RAG is a technique that enables LLMs to retrieve pertinent information from existing knowledge bases or documents based on user input. By enriching the contextual data provided to the LLM, RAG allows for far more precise and contextually relevant responses. Its applications are vast, spanning from technical documentation in development processes to answering frequently asked questions in customer support, and aiding decision-making systems based on real-time data.
The SaaS Advantage
RAG brings immense value to software-as-a-service (SaaS) providers and their tenants. Utilizing a multi-tenant architecture means that services can be delivered efficiently to multiple tenants from a single codebase. As users engage with the service, their data accumulates while remaining secure through appropriate access controls and data isolation.
Consider a customer service call center SaaS as an example. Each tenant has a unique knowledge base that includes historical inquiries, FAQs, and product manuals. By employing a RAG system, the LLM can generate responses that are customized to the tenant’s context by referencing these specific data sources. This level of personalization is a significant leap beyond generic AI assistants, allowing businesses to tailor interactions to better reflect their brand and customer needs.
Technical Challenges: Security and Privacy
However, the implementation of RAG is not without its hurdles, particularly concerning security and privacy. The primary challenge lies in maintaining data isolation between tenants and preventing unintended data leakage or unauthorized cross-tenant access. In a multi-tenant environment, ensuring robust data security is crucial to preserving trust and maintaining a competitive edge for SaaS providers.
Simplifying RAG with Amazon Bedrock
Amazon Bedrock Knowledge Bases can streamline the RAG implementation process, particularly when using OpenSearch as a vector database. Providers have two primary options: Amazon OpenSearch Service and Amazon OpenSearch Serverless. Both differ in characteristics and permission models, highlighting the importance of choosing the best approach for your multi-tenant setup.
Leveraging JWT and Fine-Grained Access Control (FGAC)
In this ecosystem, we can explore patterns for tenant isolation using a combination of JSON Web Tokens (JWT) and fine-grained access control (FGAC). The solution involves implementing OpenSearch Service as the vector database while utilizing AWS Lambda as the orchestration layer to manage processes.
The Power of JWT
JWTs are self-contained tokens that facilitate secure data isolation and access control in multi-tenant environments. The advantages of using JWT include:
- Dynamic Tenant Identification: JWT payloads can include tenant-specific attributes, allowing the system to dynamically identify tenants during each request.
- Integration with FGAC in OpenSearch: This allows roles to be mapped according to tenant IDs indicated in the JWT, resulting in precise control over data access.
Combining JWT with FGAC empowers organizations to create robust, scalable data isolation in a multi-tenant RAG environment using OpenSearch Service.
RAG Solution Architecture
In a RAG system, documents that augment LLM outputs are vectorized and indexed in a vector database. User queries are converted into vectors and searched in the database. The retrieved data enhances the LLM’s responses, leading to more contextually relevant interactions.
Key Solution Workflow:
- User Authentication: Users are created in an Amazon Cognito user pool, receiving a JWT during login that includes tenant ID information.
- Query Processing: User queries are sent to a Lambda function through Amazon API Gateway along with the JWT.
- Vectorization and Retrieval: The query is vectorized using a text embedding model, with domain and index information obtained from DynamoDB.
- LLM Interaction: Retrieved information is integrated into the prompt for the LLM to generate tailored responses.
Isolation Models in OpenSearch Service
OpenSearch Service supports multiple isolation models:
- Domain-level isolation: Each tenant is assigned a dedicated OpenSearch domain.
- Index-level isolation: Separate indexes are created for each tenant within a shared domain.
- Document-level isolation: Multiple tenants use shared indexes, with access governed by FGAC to secure document access.
Considerations and Best Practices
While the presented solution leverages shared resources like DynamoDB tables and S3 buckets, consideration should be given to partitioning models for production scenarios. Additional strategies, such as dynamically generating IAM policies, may bolster access control.
Conclusion
This exploration highlights how RAG can significantly enhance LLMs when combined with Amazon OpenSearch Service. The integration of JWT and FGAC for tenant data isolation fosters a secure, efficient environment for multi-tenant applications. By adopting these strategies, SaaS companies can deliver personalized AI capabilities that cater to individual tenant needs, all while overcoming the complexities associated with data isolation and security.
Stay tuned for further discussions on implementing RAG in multi-tenant architectures, and make sure to check out the GitHub repository for technical specifications and deployment instructions!
About the Authors
Kazuki Nagasawa is a Cloud Support Engineer at Amazon Web Services, specializing in OpenSearch Service and dedicated to solving technical challenges for customers. Outside of work, he enjoys exploring whiskey varieties and discovering new ramen spots.
Kensuke Fukumoto serves as a Senior Solutions Architect at AWS, where he’s passionate about assisting ISVs and SaaS providers in modernizing their applications. In his downtime, he enjoys riding motorcycles and visiting saunas.
For further resources and insights into implementing multi-tenant RAG, explore additional readings and documentation available on the AWS platform. Happy coding!