By Purush Das • March 15, 2024
In this blog, we will explore how to connect Foundation Models to your enterprise data sources using Amazon Bedrock’s Knowledge base.
Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon via a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI.
With Knowledge Bases for Amazon Bedrock, you can give FMs and agents contextual information from your enterprise’s data sources for Retrieval Augmented Generation (RAG) to deliver more relevant, accurate, and customized responses.
To equip FMs with up-to-date and proprietary information, organizations use Retrieval Augmented Generation (RAG), a technique that fetches data from company data sources and enriches the prompt to provide more relevant and accurate responses. Knowledge Bases for Amazon Bedrock is a fully managed capability that helps you implement the entire RAG workflow from ingestion to retrieval and prompt augmentation without having to build custom integrations to data sources and manage data flows. Session context management is built in, so your app can readily support multi-turn conversations.
Create a S3 bucket for your Data Source
Create a Lambda function that calls the Knowledge Base via Bedrock APIs using the boto3 client.
In this blog, we explored how to use Amazon Bedrock Knowledge base, OpenSearch Serverless, S3, and Lambda to implement Retrieval-Augmented Generation (RAG) that can be used via API.
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