What are Amazon Bedrock Knowledge Bases?

Key takeaways:

• Knowledge bases are digital libraries that store and organize data, helping AI provide accurate and context-specific answers.

• RAG is a technology that combines AI with knowledge bases to give precise and relevant responses by pulling real data before generating answers.

• Bedrock Agents are smart tools that automate tasks, handle queries, and connect with knowledge bases to provide quick and efficient solutions.

• Bedrock Guardrails is a tool for imposing safety checks, ensuring AI responses are accurate, relevant, and follow ethical and legal standards.

Large language models are deep learning models that can identify patterns and generate responses in human language. These models are trained on vast amounts of data, making them ideal for general, everyday tasks. On one hand, their broad knowledge is what makes LLMs so popular. But that same versatility is also their biggest limitation regarding specific scenarios. Without domain-specific context, they can produce irrelevant or even incorrect answers—often called hallucinations. This is where knowledge bases come into the picture.

So, let’s dive in!

What is a knowledge base?#

A knowledge base is a centralized repository where information is stored, organized, and retrieved. Think of it as a digital library that ensures all organizational data—documents, FAQs, best practices, or customer support details—remains accessible. We can connect knowledge bases with general-purpose foundation models to provide internal or use-case-specific information, improving their responses.

Understanding Amazon Bedrock Knowledge Bases#

Studies indicate that 85% of businesses believe well-managed knowledge bases improve decision-making. Amazon Bedrock Knowledge Bases is a fully managed capability from AWS that simplifies connecting AI models and agents (discussed later) to your company data, allowing businesses to integrate their unique data with powerful AI models. It also speeds up time to market by simplifying pipeline development and reducing build time with an out-of-the-box solution. For example,

• Developers can retrieve relevant documentation and template examples directly in their IDEs, allowing them to develop applications faster.

• Businesses can reduce development costs using a pretrained model that dynamically incorporates updated or private data from the knowledge base, eliminating the need for ongoing model training.

Key features of Amazon Bedrock Knowledge Bases#

Amazon Bedrock Knowledge Bases come with the following key features:

• Support structured (databases, spreadsheets, etc.) and unstructured (text documents, images, etc.) data.

• Support integration with popular FMs for diverse generative AI capabilities.

• Use vector databases to understand semantic relationships.

Workflow of creating a knowledge base#

Bedrock Knowledge Bases follows several key steps to optimize data for efficient retrieval.

Here’s a brief overview:

1. Data sources: Gather relevant data (e.g., FAQs, product info) to build your knowledge base, ensuring accuracy for better answers.

2. Data ingestion: Connect Bedrock to the data sources like Amazon S3, Salesforce, etc.

3. Data processing: Convert data into vectors that capture meaning, allowing for more accurate search results based on context.

4. Vector store: Store these embeddings in a specialized database, making it easy to retrieve relevant information.

Creating a knowledge base in the AWS Management Console#

Following the steps shown in the illustrations below to create a knowledge base using the AWS Management Console:

When a user query is made, it is converted into a vector and compared to the stored embeddings. Relevant information is retrieved and enhanced with context to generate a complete response. Let’s see how this is done.

Retrieval-augmented generation (RAG)#

A typical generative AI model (like GPT-based models) generates text based on patterns learned during training without explicitly referencing domain-specific data. Retrieval-augmented generation (RAG) is a technique used to enhance the performance of generative AI models by combining information retrieval and text generation. It allows the models to reference authoritative knowledge bases or internal repositories before generating responses, producing more relevant, accurate, and context-specific output.

In other words, RAG-based models retrieve critical data from knowledge bases to ensure that the generated content is accurate and contextually relevant. Let’s understand how the entire process unfolds.

How does RAG work?#

A RAG technique combines data retrieval with AI-generated responses. It first performs a retrieval step to fetch contextually relevant information from a knowledge base, database, or external repository. Once the relevant data is retrieved, the model uses it to provide a more comprehensive, relevant, and explainable response to the user.

Here’s a brief overview of the process mentioned above:

• Query embedding: To capture the semantic meaning of the user query, it is first transformed into a vector representation using an embedding model.

• Contextual data retrieval: The generated query vector searches the knowledge database for semantically similar text chunks, identifying the information closely related to the user’s query.

• Prompt augmentation: Related embeddings are then retrieved to combine with the original user query and create an augmented prompt for the model enriched with additional context.

• Response generation: The augmented prompt is input into a foundation model within AWS Bedrock, which generates a contextually relevant response based on the user’s query and the retrieved data.

Real-world applications with RAG#

Here are some of the most impactful use cases where the RAG technique is driving innovation and delivering transformative outcomes:

• Health care: The RAG applications can process complex medical documents, allowing healthcare providers to quickly extract and summarize patient information. For example, a hospital can use a RAG-based model to help doctors diagnose unclear symptoms by retrieving clinical studies, diagnostic steps, and treatment options, improving accuracy for rare conditions like autoimmune encephalitis.

• Customer service: By integrating knowledge bases, customer service agents can access up-to-date product information, troubleshoot issues, and provide personalized support, enhancing the overall customer experience. For example, a global electronics retailer can use an RAG assistant to guide customers through product issues, like providing step-by-step fixes for smart home device connectivity problems.

• Personalized learning: Schools use RAG applications to deliver personalized learning experiences. Assume an EdTech startup uses a RAG-powered tutor to simplify studying. For example, a student preparing for a chemistry exam can ask about titration, and the assistant provides examples, diagrams, and practice problems from reliable sources.

• Business intelligence: Organizations can query structured data like sales records to generate insightful reports dynamically, aiding in strategic decision-making. For instance, a credit card company uses a RAG-based model to analyze spending patterns to predict future trends, enabling targeted marketing campaigns.

Are you excited to innovate with the RAG technique? Let’s explore our hands-on Cloud Labs and discover its full potential.

RAG-based models provide contextual responses— but what if the system could also make autonomous decisions and solve problems independently? Wouldn’t that be great? Let’s see how this is possible.

Autonomous decision-making with Bedrock Agents#

Amazon Bedrock Agents represent one of the more sophisticated features of AWS Bedrock. They act as smart task managers, breaking instructions into smaller steps and connecting with various resources to get things done automatically. Best of all, AWS Bedrock takes care of the infrastructure, coding, and security, making it easier and faster to deploy generative AI applications.

Example: Customer service with Bedrock Agents#

Imagine working in a global e-commerce company; your company handles millions of customer requests daily—questions like:

• “Where is my order?”

• “How do I return this product?”

• “Can you recommend a product for me?”

Handling this volume only with human agents will result in long wait times, and delayed responses will frustrate customers and cause revenue loss.

We can easily overcome this problem by implementing AWS Bedrock Agents to automate customer service workflows. They can act as virtual assistants, answering customer queries by retrieving information from databases, making API calls, and invoking knowledge bases to perform actions like suggesting products, tracking orders, and issuing refunds.

Wonder how this process would work? Let’s find out.

How does it work?#

The following diagram represents an overview of the workflow for AWS Bedrock Agents automating customer service:

AWS Bedrock Agents makes the entire customer service process seamless and efficient. Here’s a brief description of the above diagram:

• User interaction layer: Customers interact with the agent through a web or mobile app, voice assistant, or chatbot (e.g., Amazon Lex).

• Input processing: The user input is validated and categorized into predefined intents (e.g., tracking, returns, or troubleshooting).

• AWS Bedrock Agent: The Bedrock Agent is the core orchestrator, interpreting user input and deciding which actions to perform. For example, the agent fetches return policies and generates return shipping labels if the intent is to return.

• Data sources: The agent connects to various data sources, such as CRM, order databases, and inventory systems, to retrieve and update information.

• Task orchestration: The agent invokes the foundation model to process user queries and predict the best action.

• Action execution: The agent may communicate with another agent or directly trigger a Lambda function to execute tasks such as querying order statuses, issuing refunds, or updating customer profiles.

• Response delivery: The agent repeats the steps until all tasks are completed, then compiles and sends dynamic responses back to the user through the interaction layer.

Until now, we’ve explored adding context to generative AI responses with knowledge bases and RAG and how to streamline and automate tasks in real-world scenarios using agents. But wouldn’t it be great if we could also ensure that the output of an FM is accurate and reliable? Well, AWS has got you covered!

Ready to build smarter applications? Let’s explore a hands-on approach with Amazon Bedrock.

Amazon Bedrock Guardrails#

Amazon Bedrock Guardrails provides configurable safeguards for generative AI applications, ensuring that AI models function within specified limits while upholding ethical, legal, and security standards. They enhance the safety and transparency of foundation model (FM) outputs through contextual grounding checks. These checks operate on two key parameters:

• Grounding: This check ensures that the response is accurate per the reference; unsupported details are flagged as ungrounded. For example, if the reference says, “U.S. customers have a 20-day return window,” and the user asks, “What’s the return policy in the EU?” saying, “EU customers have a 20-day return window,” is ungrounded and wrong.

• Relevance: Checks if the response directly answers the query; irrelevant information is flagged, even if accurate. For example, if a user asks a chatbot, “What’s the return policy in the U.S.?” and it responds, “EU customers have a 14-day return window,” the answer is irrelevant. Instead, the chatbot should provide U.S. customers with a 20-day cooling-off period, ensuring the response aligns with the user’s specific region.

Guardrails can be associated with your agent during creation or update. They allow you to set confidence thresholds for grounding and relevance scores to filter out low-quality responses, ensuring the AI provides accurate and relevant information and mitigating hallucinationsLLMs are prone to generating information that is not supported by the source data, resulting in false, non-existent, and incorrect results called hallucinations..

Conclusion#

Amazon Bedrock Knowledge Bases revolutionize how organizations manage and leverage their data, enabling precise, context-aware responses tailored to specific use cases. Businesses can generate accurate, relevant, and efficient outputs by integrating internal data with foundation models and utilizing techniques like Retrieval Augmented Generation (RAG). Bedrock Agents take this further by automating complex workflows, streamlining decision-making, and enhancing customer experiences. Additionally, Amazon Bedrock Guardrails ensure AI outputs remain grounded, relevant, and compliant, addressing accuracy and ethical use concerns. In a nutshell, Amazon Bedrock Knowledge Bases are transforming the way organizations leverage AI, making them an indispensable tool for innovation and efficiency.