A 360-Degree Profile of Amazon Bedrock Knowledge Bases
Unlocking grounded intelligence with Retrieval-Augmented Generation (RAG) on your enterprise data
Published Apr 3, 2025
The aim of this blog is to provide a comprehensive guide for using Amazon Bedrock Knowledge Bases to ground foundation model outputs in enterprise data, enabling developers to build accurate, context-aware GenAI applications.
Generative AI (GenAI) is transforming industries, enabling businesses to automate tasks, answer complex questions, and build intelligent digital assistants. However, many organizations struggle to align these models with their internal knowledge and context. While foundation models are trained on vast public datasets, they lack access to private enterprise data, which limits the trustworthiness and specificity of their responses.
Amazon Bedrock solves this problem through Knowledge Bases, which enable Retrieval-Augmented Generation (RAG) in a fully managed, no-code/low-code fashion. With Knowledge Bases, foundation models can search and reference your documents or databases in real time—delivering answers that are both relevant and grounded.
As enterprises adopt generative AI, they face challenges in ensuring model responses are grounded in their internal data. Unstructured documents, data fragmentation, and integration complexity make it difficult to build trustworthy GenAI applications.
To solve this problem, I show readers how to build a knowledge base using Amazon Bedrock, embed their data into vector stores, and connect this knowledge to GenAI-powered chatbots using AWS Amplify or Amazon Lex.
Amazon Bedrock launched Knowledge Bases to simplify the implementation of Retrieval-Augmented Generation (RAG). Rather than requiring manual pipelines for chunking, embedding, storing, and retrieving knowledge, Bedrock offers a fully managed flow from data ingestion to model inference.
Amazon Bedrock Knowledge Bases support:
- Unstructured data (via vector store)
- Structured data (via Amazon Redshift)
- Enterprise search (via Amazon Kendra GenAI Index)
Each type supports secure, real-time augmentation of model prompts using Bedrock’s `RetrieveAndGenerate` API.
Before building a knowledge base, developers can explore RAG and prompt engineering directly in the Amazon Bedrock playground:
- Choose a foundation model (e.g., Claude, Titan).
- Use the Prompt tab to simulate an FM call.
- Use the RAG tab to input sample documents and test `RetrieveAndGenerate` API.
- Observe how retrieved chunks affect the model’s output.
This lets teams prototype before setting up data pipelines.
How to Implement with Bedrock Knowledge Bases
Amazon Bedrock Knowledge Bases let you operationalize RAG for production workloads. Here’s how:
Step 1: Choose a Knowledge Base Type
| Type | Use Case |
|---|---|
| Vector Store | Unstructured documents (PDF, TXT, DOCX, HTML) |
| Structured Data Store | Tabular/relational data in Amazon Redshift |
| Kendra GenAI Index | Enterprise semantic search powered by Amazon Kendra |
Step 2: Connect a Data Source
For vector-based knowledge bases, you can pull from:
- Amazon S3
- Confluence
- Salesforce
- SharePoint
- Web crawlers or custom connectors
For structured data, connect your Amazon Redshift cluster or data warehouse.
Step 3: Parse and Chunk Documents
- Bedrock uses built-in parsers for formats like PDF, DOCX, HTML, and TXT.
- Chunking splits content into overlapping text windows (e.g., 500–800 tokens, with ~100-token overlap).
- Multimodal support (e.g., tables, diagrams) is only available via Amazon S3 or custom connectors.
Step 4: Select an Embedding Model
Choose from:
- Amazon Titan Text Embeddings V1/V2 – AWS-native, cost-optimized
- Cohere Embed English V3 – Optimized for English content
- Cohere Embed Multilingual V3 – Best for global language support
These models convert text chunks into vector embeddings for semantic search.
Step 5: Choose and Connect a Vector Store
Quick Create (fully managed):
- Amazon OpenSearch Serverless
- Amazon Aurora PostgreSQL Serverless
- Amazon Neptune Analytics (GraphRAG)
Bring Your Own (BYO):
- Pinecone
- MongoDB Atlas Vector Search
- Redis Enterprise Cloud
Vector stores index and retrieve relevant chunks at runtime. Syncs can be managed manually or automatically based on the data source.
Step 6: Query the Knowledge Base
Use Bedrock’s `RetrieveAndGenerate` API to:
- Search the vector store or structured database.
- Append retrieved chunks to the FM prompt.
- Generate grounded, accurate responses.
Step 7: Build a Chatbot with Amplify or Lex
Amazon Amplify + Bedrock
- Build a front-end UI using React or Next.js.
- Host with AWS Amplify + Amazon CloudFront.
- Use API Gateway + Lambda to call Bedrock Knowledge Base.
[See reference blog →](https://aws.amazon.com/blogs/mobile/creating-a-generative-ai-travel-assistant-app-with-amazon-bedrock-and-aws-amplify/)
Amazon Lex + Bedrock
- Create a conversational bot with Amazon Lex.
- Use Lex intent triggers to call a Lambda function.
- Call `RetrieveAndGenerate` and return the response.
[See reference blog →](https://aws.amazon.com/blogs/machine-learning/build-a-self-service-digital-assistant-using-amazon-lex-and-amazon-bedrock-knowledge-bases/)
Best Practices
| Area | Recommendation |
|---|---|
| Parsing | Use S3 for complex docs; ensure consistent format across sources |
| Chunking | Use 500–800 token chunks with 100-token overlap for optimal RAG performance |
| Embeddings | Titan for performance, Cohere for multilingual |
| Vector Stores | OpenSearch for native integration, Pinecone for low-latency apps |
| Syncing | Use automated sync for S3; monitor source updates for manual triggers |
| Prompt Design | Use templates with citations and context formatting |
Amazon Bedrock Knowledge Bases make it easy for builders to integrate enterprise knowledge with powerful foundation models. By enabling Retrieval-Augmented Generation (RAG) over structured and unstructured content, Bedrock empowers developers to build GenAI-powered applications that are accurate, explainable, and grounded in truth.
Whether you're deploying a chatbot, internal assistant, or customer-facing GenAI experience, Bedrock Knowledge Bases provide the foundation for secure, scalable innovation.
Next Steps
- Try [Amazon Bedrock in the Console](https://signin.aws.amazon.com/signup?request_type=register)
- Read the [official documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/knowledge-base-build.html)
Explore integration with [Amplify](https://docs.amplify.aws) and [Lex](https://docs.aws.amazon.com/lex/latest/dg/what-is.html)