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Guidance for Custom Search of an Enterprise Knowledge Base with Amazon OpenSearch Service

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Overview

This Guidance demonstrates how to build an application for search based on the information in an enterprise knowledge base through the deployment of interface nodes, including large language models (LLMs). You can combine services to give answers to questions based on your enterprise knowledge base with a search engine that provides word segmentation search, fuzzy queries, and artificial intelligence (AI) assisted capabilities. This Guidance also includes methods such as manual labeling, unsupervised clustering, supervised classification, and an LLM to extract guide words. Deploying this Guidance can help you automatically split documents into paragraphs with embedded vectors to further establish a structured enterprise knowledge base.
Please note: [Disclaimer]

How it works

These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.

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Deploy with confidence

Ready to deploy? Review the sample code on GitHub for detailed deployment instructions to deploy as-is or customize to fit your needs. 

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Well-Architected Pillars

The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.

All the services used in this Guidance, such as Lambda and API Gateway, provide Amazon CloudWatch metrics that can be used to monitor individual components of the Guidance. API Gateway and Lambda allow for publishing of new versions through an automated pipeline. CloudWatch is available for Amazon Connect, Amazon Lex, and Amazon Kendra, enabling monitoring, metric collection, and performance analysis for these services.

Read the Operational Excellence whitepaper

AWS Identity and Access Management (IAM) is used in this Guidance to control access to resources and data. API Gateway helps with security by providing a protection layer for invoking category services through an outbound API. It acts as a gateway, or a proxy, between the client and the backend services, allowing you to control access and implement security measures. 

Read the Security whitepaper

The services used in this Guidance are Lambda, DynamoDB, Amazon S3, and SageMaker. These services provide high availability within a Region, and allow deployment of highly available SageMaker endpoints. We use these services to implement a reliable application-level architecture by ensuring loosely coupled dependencies, handling throttling and retry limits, and maintaining stateless compute capabilities.

Read the Reliability whitepaper

This Guidance requires near real-time inference and high concurrency. Lambda, DynamoDB, and API Gateway are designed to meet this criteria. Also, we use SageMaker to host the LLM as an endpoint. Amazon Kendra and OpenSearch are ideal services for the concept of RAG. RAG combines retrieval-based models and language generation to improve generated text. Amazon Kendra and OpenSearch are utilized for efficient knowledge retrieval. This architecture enables the system to leverage retrieved information for more accurate and contextually relevant text generation.

Read the Performance Efficiency whitepaper

This Guidance uses Lambda to design all compute components of search and question and answer, keeping billing to pay per millisecond. The data store is designed using DynamoDB and Amazon S3, providing a low total cost of ownership for storing and retrieving data. The Guidance also uses API Gateway, which reduces API development time and helps you make sure you only pay when an API is invoked.

Read the Cost Optimization whitepaper

This Guidance uses the scaling behaviors of Lambda, a SageMaker inference endpoint, and API Gateway to reduce over-provisioning resources. The serverless services, such as Lambda and API Gateway, are invoked only when there is a user query. It uses AWS Managed Services (AMS) to maximize resource utilization, and to reduce the amount of energy needed to run a given workload. Amplify, Amazon Connect, and Amazon Lex leverage auto-scaling capabilities to continually match the load and allocate resources accordingly. By dynamically adjusting resource levels based on demand, these services ensure that only the minimum necessary resources are utilized, optimizing efficiency and cost-effectiveness.

Read the Sustainability whitepaper

Disclaimer

The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.References to third-party services or organizations in this Guidance do not imply an endorsement, sponsorship, or affiliation between Amazon or AWS and the third party. Guidance from AWS is a technical starting point, and you can customize your integration with third-party services when you deploy the architecture.