Amazon Bedrock Knowledge Bases: Unlocking the Power of Customized AI Insights

Amazon Bedrock Knowledge Bases make it easy to connect your private data with AI models, improving accuracy and relevance without complex setup. This guide breaks down how they work, their key benefits, and how to get started.

In this article, you will learn:

• Understanding Amazon Bedrock Knowledge Bases
• Retrieval Augmented Generation (RAG)
• Vector Database
• What are Bedrock Knowledge Bases
• Practical Use Cases for Amazon Bedrock Knowledge Bases
• Benefits and Challenges of Using Bedrock Knowledge Bases
• Benefits
• Challenges
• Step-by-Step Setup of a Bedrock Knowledge Base
• Conclusion

Recently, we published a post highlighting what you can achieve with Amazon Bedrock and how it provides the easiest way to get started with generative AI on AWS. Bedrock offers a wide range of features designed to simplify these steps, with one key advantage being its seamless integration with other AWS services. Bedrock Knowledge Bases add even more value by indexing your private data, making it available for use with AI models or Bedrock Agents.

Understanding Amazon Bedrock Knowledge Bases

Knowledge bases in Amazon Bedrock are a capability that enables you to implement Retrieval Augmented Generation (RAG) for your generative AI applications. Bedrock’s other features are designed to be simple and accessible for those who don’t have coding experience.

Let’s define a couple of relevant words like Retrieval Augmented Generation (RAG) and Vector Database.

Retrieval Augmented Generation (RAG)

RAG allows AI models to access and utilize up-to-date, domain-specific information that may not be part of their original training data. In Bedrock, this process is automatized:

• Retrieval: The system searches for relevant information from a designated data source.
• Augmentation: The retrieved information is added to the input prompt or query.
• Generation: The LLM uses the augmented input to generate a more informed and accurate response.

Vector Database

When establishing a connection between a Bedrock Knowledge Base and a data source, it is essential to configure a vector database. A vector database is a specialized type of database with embeddings of mathematical representations of data designed to store, update, and manage them.

What are Bedrock Knowledge Bases

Here's an overview of what knowledge bases are and how they work:

• Knowledge bases allow you to integrate your information (documents, texts, etc) into AI applications, enhancing the relevance and accuracy of generated responses.
• They simplify the setup and execution of the RAG workflow, which involves retrieving relevant information from a data store to augment responses generated by Large Language Models (LLMs).
• Your application can query the knowledge base to retrieve information.
• Knowledge bases can be added to Amazon Bedrock agents, allowing the agent to identify and retrieve relevant information based on user input.

Bedrock Knowledge Bases help you to handle tasks like:

• Fetching documents from your specified data source,
• chunking text into manageable pieces,
• converting and storing text as vector embeddings.

Practical Use Cases for Amazon Bedrock Knowledge Bases

Knowledge bases are particularly useful for building applications that require context from proprietary private data, such as customer support systems, internal knowledge management tools, or domain-specific chatbots.

• Customer support systems: For example, a support agent using a knowledge base can quickly find solutions to common customer issues or access product-specific information to address inquiries.
• Internal knowledge with provisioned feedback: Knowledge bases are useful when a company already has data containing its internal knowledge. A dedicated system (or worker) can analyze this historical data and create informed decisions for future projects. For instance, based on doctors' past decisions and diagnoses in a clinic, the knowledge base can build a repository to guide future decision-making processes.
• Domain-specific chatbots: In finance, a chatbot could leverage a knowledge base to answer customer questions about tax regulations, loan applications, or investment strategies. By integrating domain-specific knowledge, these chatbots can deliver personalized and accurate responses, improving the overall user experience.

Benefits and Challenges of Using Bedrock Knowledge Bases

Benefits

These are the main benefits of using Bedrock Knowledge Bases:

• Faster time to market by providing an out-of-the-box RAG solution.
• Increased cost-effectiveness by eliminating the need for continuous model training on private data.
• Improved response accuracy and relevance by leveraging your own data.

Challenges

• Data Security and Privacy: Integrating sensitive data into a knowledge base requires a careful approach to security and privacy. Organizations must ensure compliance with data protection regulations (e.g., GDPR, HIPAA) and implement robust encryption and access controls. With Amazon Bedrock, these concerns can be mitigated using AWS’s enterprise-grade security features. It will be easier to get a secure environment if you already have your data in AWS. But it’s also possible to connect it securely to an on-premises or any other cloud environment.
• Technical Complexity: Setting up and managing a knowledge base for Amazon Bedrock requires expertise in AWS and integration. Organizations may need skilled teams or managed services to overcome these technical hurdles effectively. However, as mentioned earlier, it’s significantly easier to get started with Bedrock than other methods.

Step-by-Step Setup of a Bedrock Knowledge Base

Here you can find the step-by-step manual on how to start with Bedrock Knowledge Bases:

1. Log in to the AWS Console and go to Amazon Bedrock (Amazon Bedrock models are not available in all AWS regions).

2. Select Knowledge Bases from the left panel.

3. Click on “Create” and “Vector Store”.

4. You either leave everything on default or select options as you need. We’ll be using S3 as a data source.

5. Configure your data source.

6. Select the embeddings model and vector store.

You need to make sure that you understand the database pricing. Amazon OpenSearch Serverless, for example, costs around $100 per month, even without any usage.

7. Review and create the Knowledge Base and start using it with AWS services, like Amazon Bedrock Agents.

Conclusion

Amazon Bedrock Knowledge Bases simplify the process of integrating private, domain-specific data into AI applications, enabling businesses to leverage Retrieval Augmented Generation (RAG) workflows with minimal effort. Although challenges like data security and technical complexity exist, Bedrock Knowledge Bases offer a practical and accessible solution for unlocking the full potential of customized AI insights.