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This solution is particularly suited for organizations looking to streamline document and relational data access across large datasets, such as data warehouses and document repositories, using conversational AI to enhance user experiences.
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
These features collectively enhance usability, security, and efficiency, making the solution adaptable to diverse business environments.
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.
Extract information from databases using natural language.
Users engage with the system through natural language queries, receiving precise responses augmented by the knowledge embedded within corporate documents.
Documents are tagged and filtered based on metadata, allowing the system to retrieve the most relevant information.
The system takes into account the conversational history to provide more accurate, context-aware responses, improving the overall user experience.
Built-in token, user, and role-based limitations help manage API usage and prevent excessive costs, ensuring that resource consumption is kept under control.
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.
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.
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.
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.
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.
Architecture
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.
Critical services, such as the RAG chain, document processing, and the conversational interface, are deployed as scalable containers, ensuring efficient resource management.
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.
Comprehend – Identifies and redacts PII as well as flags for inappropriate content.Polly – text-to-speech functionality for AI responses increases the accessibility of the system.
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 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 performanceand logging, providing real-time insights and alerts to quickly resolve issues.
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 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.
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.
Leveraging AWS’s managed services, we prioritize optimal resource allocation, scalability, and monitoring to adapt to evolving workload demands effectively.
Auto Scaling: ECS is configured with auto-scaling policies to dynamically adjust the number of tasks based on load, ensuring resources are provisioned efficiently and only when needed.
Dynamic Load Distribution: ALBs distribute incoming traffic across multiple ECS tasks, improving performance by ensuring that no single instance is overwhelmed.
On-Demand and Provisioned Capacity Modes: Based on workload patterns, DynamoDB can be configured to use either provisioned capacity (for predictable workloads) or on-demand mode (for variable or unpredictable workloads), ensuring efficient performance without over-provisioning.
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.
LangChain for RAG
The solution leverages LangChain to implement advanced Retrieval-Augmented Generation capabilities. LangChain serves as the orchestrator for managing queries, retrieving relevant documents, and contextualizing responses from the LLM.
SQL Agent
In case the retrieval is from a SQL database, we can operate a SQL agent which can transform natural language to SQL and get information out of a database.
Document Ingestion Pipeline
A pipeline automates the ingestion of documents from Amazon S3. These documents are then embedded using an LLM model to create vector representations, which are stored in PGVector for fast retrieval.
User Interaction via React Client
Users interact with the system via a responsive React frontend, which is designed to provide intuitive conversational interactions. The system tracks the conversation history, and context is maintained to improve response relevance over time.
Testing
The solution uses a self hosted LangFuse instance to automate RAG correctness testing, utilizing a set of test questions developed by subject matter experts (SMEs). These tests evaluate document retrieval accuracy, relevance, and contextuality based on real-world use cases. The results provide feedback for continuous improvement, ensuring high performance and reliability in the system’s responses.
Tracing
LangFuse also provides LLM tracing and troubleshooting capabilities. This can help administrators and developers in getting insight into the system. It can also provide cost information related to the LLM usage. The trace information never leaves the cluster and remains private.
Retrieval-Augmented Generation (RAG) Platform FAQ
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.
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.
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.
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.
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.
Solution Brief
FAQ’s
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