Solutions

This solution is particularly suited for organizations looking to streamline document access across large datasets, such as internal corporate manuals, policies, or any document repository, using conversational AI to enhance user experiences.

An abstract illustration features a black container with a U-shaped cut. From the top, purple lines with connected dots extend outward in various directions, resembling circuit pathways or network connections. The overall design represents technology and connectivity, capturing the essence of an advanced RAG Platform.

72%

of organizations Globally

have adopted AI in at least one business function

60%

of large enterprises using generative AI

have now integrated RAG to enhance the accuracy of AI outputs

50%

reduction rate in hallucinations

with inaccurate outputs in generative AI models across industries

30-40%

enterprises deploying RAG Frameworks

reported a 30-40% reduction in retraining costs

Key Features

These features collectively enhance usability, security, and efficiency, making the solution adaptable to diverse business environments.

Real-time Document Retrieval

Leveraging RAG (Retrieval-Augmented Generation) technology, the solution enables users to interact in real-time with a conversational interface to retrieve documents based on context and intent.

Advanced Metadata Filtering

Documents are tagged and filtered based on metadata, allowing the system to retrieve the most relevant information.

History-aware Retriever 

The system takes into account the conversational history to provide more accurate, context-aware responses, improving the overall user experience.

Cost Control

Built-in token, user, and role-based limitations help manage API usage and prevent excessive costs, ensuring that resource consumption is kept under control.

Conversational Interactions

Users engage with the system through natural language queries, receiving precise responses augmented by the knowledge embedded within corporate documents.

Highly Customizable

The solution offers a high degree of flexibility, allowing users to configure document ingestion, retrieval parameters, and role-based access controls to suit specific organizational needs. Custom workflows can be designed for different departments, ensuring that each user group accesses relevant information tailored to their roles. This adaptability ensures the solution fits seamlessly into diverse business environments.

Fully Automated

Infrastructure is provisioned using an IaC platform, enabling easy replication, deployment, and management of the system. This reduces the risk of manual configuration errors and ensures consistent deployments across multiple environments.

User Authentication and Access Control

The solution integrates with AWS Cognito and corporate Single Sign-On (SSO) systems to provide secure and seamless user authentication. This ensures that only authorized users can access the system.

Data Protection 

Sensitive data, such as conversation histories and document embeddings, are securely stored in DynamoDB and PostgreSQL, respectively. Encryption at rest and in transit is employed to protect all stored information.

RAG Correctness Testing

The solution includes built-in tools for evaluating the accuracy of the Retrieval-Augmented Generation (RAG) system. This feature enables admins and SMEs to test the system’s performance against predefined benchmarks or real-world use cases, ensuring that document retrievals and conversational responses are contextually accurate and reliable.0

The React frontend client is served via CloudFront, ensuring fast and reliable delivery to end users.The React frontend client is served via CloudFront, ensuring fast and reliable delivery to end users.

User authentication is handled by Cognito, with corporate SSO integration for secure access management.The React frontend client is served via CloudFront, ensuring fast and reliable delivery to end users.

Critical services, such as the RAG chain, document processing, and the conversational interface, are deployed as scalable containers, ensuring efficient resource management.The React frontend client is served via CloudFront, ensuring fast and reliable delivery to end users.

This stores document embeddings for fast, efficient vector retrieval. The PGVector extension in PostgreSQL enables seamless vector-based searches, critical for RAG performance.

Used as the core LLM (Large Language Model) provider for the system’s language understanding capabilities. It ensures conversational responses are accurate and natural, while maintaining data privacy.

Conversation histories are stored in DynamoDB, which enables efficient and scalable tracking of user interactions.

Well-Architected Pillars

The AWS Well-Architected Framework helps you understand the pros and cons of the decisions you make when building systems in the cloud. The six pillars of the Framework allow you to learn architectural best practices for designing and operating reliable, secure, efficient, cost-effective, and sustainable systems. The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind.

Operational Excellence

Operational Excellence in this solution is achieved through automation, monitoring, and continuous improvement.

By leveraging AWS services like Amazon ECS and RDS, the system ensures scalable management of backend services, automated backups, and failover handling.

Amazon CloudWatch is used for monitoring application performance
and logging, providing real-time insights and alerts to quickly resolve issues.

Data privacy is maintained with Amazon Bedrock, ensuring that sensitive information is processed securely within the AWS environment.

The architecture also supports auditing via DynamoDB, which tracks session history, allowing for detailed analysis and continuous optimization of user interactions and system processes, ensuring high performance and reliability over time.

Security

Security is a critical component of this solution, with multiple layers of protection built into the architecture.

Amazon Cognito integrates with Microsoft Active Directory using OAuth2 and OIDC, ensuring secure authentication and access control through corporate credentials.

Amazon CloudFront provides additional security by protecting the React client from DDoS attacks, while AWS WAF safeguards the application from web threats.

Data privacy is maintained with Amazon Bedrock, ensuring that sensitive information is processed securely within the AWS environment.

Encryption is enforced for data at rest and in transit, with detailed audit trails and access controls managed through services like AWS Identity and Access Management (IAM) and DynamoDB for session history storage.

Reliability 

RReliability in this solution is ensured through the use of highly available and resilient AWS services.

Amazon ECS provides fault-tolerant and scalable infrastructure for running containerized workloads, with automatic scaling and health checks to ensure uninterrupted operation.

Amazon RDS, supporting PGVector, offers automated backups, failover support, and multi-AZ deployments, ensuring high availability for the vector database.

Amazon CloudWatch logs provide real-time visibility into system performance and health, allowing for proactive monitoring and quick resolution of any issues, further enhancing the system’s reliability.

Performance Efficiency
Cost Optimization

Cost optimization in this solution is achieved by leveraging AWS services that provide flexible scaling and efficient resource usage.

Amazon ECS Fargate is used to run containerized services, allowing the application to scale automatically based on demand, ensuring that we only pay for the compute resources we use, without the need to manage underlying infrastructure.

Additionally, documents and application data are stored in Amazon S3, which offers elastic storage, allowing the system to scale storage capacity as needed while keeping costs low by only charging for the storage and data retrieval used. This combination of serverless and scalable services helps minimize overhead and optimize overall costs.

Sustainability

Frequently Asked Questions

Retrieval-Augmented Generation (RAG) Platform FAQ

How can VividCloud help you implement a tailored knowledge retrieval solution?

VividCloud offers end-to-end support for deploying and customizing the knowledge retrieval solution to fit your organization’s specific needs. Our team provides expertise in integrating AWS services, configuring the document ingestion pipeline, and optimizing system performance for real-time retrieval and conversational interactions. We also assist with user authentication setup, cost control measures, and ensuring seamless deployment across environments, enabling your team to focus on leveraging the solution to enhance decision-making and knowledge access.

What is a Retrieval-Augmented Generation (RAG) system, and how does it benefit organizations?

A RAG system enhances knowledge access and decision-making by combining intelligent document retrieval with conversational interactions. It allows users to ask natural language questions and receive accurate, context-aware responses based on a comprehensive document repository. This reduces time spent searching for information, making it ideal for organizations needing quick access to internal knowledge.

How does the system ensure accurate document retrieval and responses?

The solution uses advanced metadata filtering and a history-aware retriever, ensuring that each query is answered with contextually relevant information. It also includes built-in tools for RAG correctness testing, allowing admins to benchmark the system’s performance and guarantee accuracy.

Can the system be customized for different organizational needs?

Yes, the solution is highly customizable. Organizations can configure document ingestion, retrieval parameters, and role-based access controls to suit their specific requirements. Custom workflows can also be designed for different departments to ensure that users access information relevant to their roles.

How does the system ensure cost control and security?

The system features built-in token, user, and role-based limitations to manage API usage and prevent excessive costs. Security is ensured through AWS Cognito and corporate SSO integration for secure user authentication, along with encryption for sensitive data, including conversation histories and document embeddings.

Download Additional Retrieval-Augmented Generation (RAG) Platform Resources

AIoT Solution Brief

Solution Brief

FAQ’s

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