What is Amazon Bedrock Knowledge Bases?
Amazon Bedrock Knowledge Bases is a RAG service (Retrieval-Augmented Generation) from AWS. The service integrates enterprise-specific documents and data into foundation models without necessarily requiring you to build and operate your own vector database infrastructure. Documents from S3, SharePoint, Confluence, or other sources are automatically processed, converted into vectors, and made available for semantic search. As a result, your AI application delivers precise, up-to-date answers backed by source citations.
AWS now distinguishes between two operating modes: the Managed Knowledge Base, where AWS fully manages ingestion, indexing, storage, and retrieval infrastructure and adds capabilities such as agentic retrieval for multi-hop reasoning and native integration with AgentCore Gateway, and the Customer-managed Knowledge Base, where you choose and configure the vector store yourself (e.g. OpenSearch Serverless, Aurora, Neptune). AWS recommends the managed option for most new projects.
The ingestion process is largely automated: documents are split into chunks according to configurable strategies, converted into vectors using an embedding model, and stored in the chosen vector store. For each query, the most relevant chunks are retrieved semantically and passed as context to the foundation model.
Core Features
- Managed and Customer-managed Knowledge Base: the Managed Knowledge Base fully handles ingestion, indexing, and retrieval, including agentic retrieval for multi-hop reasoning; the Customer-managed Knowledge Base gives you full control over the chosen vector store.
- Automated ingestion pipeline: chunking, embedding, and indexing of your documents without custom code, including configurable chunking strategies (fixed-size, semantic, hierarchical, custom via Lambda).
- Wide range of vector stores: Amazon S3 Vectors, OpenSearch Serverless, OpenSearch Managed Cluster, Aurora PostgreSQL with pgvector, Neptune Analytics, Pinecone, Redis, and MongoDB Atlas.
- GraphRAG with Neptune Analytics: automatically generated knowledge graphs improve multi-step questions that span linked documents.
- Structured data sources: natural language queries against Amazon Redshift and Amazon SageMaker Lakehouse are automatically translated into SQL (text-to-SQL).
- Multimodal processing: tables, charts, and images within documents are analyzed via Bedrock Data Automation, Smart Parsing, or vision foundation models, including source references to visual content.
- Precise retrieval and agent integration: reranking models, metadata filtering, and Guardrails integration increase relevance. The Managed Knowledge Base can be exposed natively via AgentCore Gateway as a tool for MCP-compatible agents.
Typical Use Cases
- Internal knowledge assistant: employees ask questions in natural language and receive verifiable answers from manuals, wikis, and SharePoint.
- Customer support chatbots: self-service bots access current product and contract data and reduce ticket volume.
- Research and compliance: semantic search over contracts, policies, and regulatory documents with metadata filtering by department or time period.
- Structured data analysis: business teams query data warehouses in natural language without writing SQL.
- Agentic workflows: AI agents, for example built on Amazon Bedrock AgentCore, use the knowledge base via the Gateway as a tool to bring enterprise knowledge into multi-step tasks.
Benefits
- Fully managed: no operating your own vector databases, ingestion pipelines, or scaling.
- Cost control: usage-based billing with no separate base fee for the knowledge base feature itself.
- Data sovereignty: can run in multiple EU regions; check availability per feature and region.
- Verifiable answers: source citations and reranking increase trust and traceability, while Guardrails filter sensitive content.
- Deep AWS integration: seamless connection with Amazon Bedrock AgentCore, IAM, S3, Redshift, and other services.
Integration with innFactory
innFactory supports you in designing and implementing RAG architectures based on Amazon Bedrock Knowledge Bases: from connecting your data sources and choosing the right vector store and optimal chunking strategy to GraphRAG or text-to-SQL integration. We move your solution securely into production in EU regions and continuously safeguard the quality of your retrieval results.
Typical Use Cases
Frequently Asked Questions
What is Retrieval-Augmented Generation (RAG)?
RAG combines a large language model with an external knowledge base. Instead of relying solely on knowledge learned during training, the model first retrieves relevant documents from the knowledge base for each query and uses them as context for answer generation. The result is more precise, up-to-date, and verifiable responses with source citations.
Which vector stores does Bedrock Knowledge Bases support?
Supported stores include Amazon S3 Vectors, Amazon OpenSearch Serverless, Amazon OpenSearch Managed Cluster, Amazon Aurora PostgreSQL with pgvector, Amazon Neptune Analytics (for GraphRAG), Pinecone, Redis, and MongoDB Atlas. Amazon S3 Vectors became generally available in December 2025 and, according to AWS, reduces the cost of storing and querying large vector datasets by up to 90 percent compared with dedicated vector databases.
Which data sources can I connect?
Unstructured sources include Amazon S3 (PDF, Word, HTML, CSV, JSON, Markdown, images), Confluence, SharePoint, Salesforce, and a web crawler. For structured data you can connect Amazon Redshift and Amazon SageMaker Lakehouse: natural language questions are automatically translated into SQL. Documents are automatically split into chunks, converted into vectors, and stored in the configured vector store.
What is GraphRAG in Bedrock Knowledge Bases?
GraphRAG extends classic RAG with a knowledge graph. Bedrock Knowledge Bases automatically builds a graph of entities and their relationships in Amazon Neptune Analytics and combines vector search with graph traversal. This improves answer quality for multi-step questions that need to connect information across multiple documents. GraphRAG has been generally available since March 2025.
Does Bedrock Knowledge Bases support multimodal data?
Yes. Using Amazon Bedrock Data Automation or vision foundation models (e.g. Claude, Amazon Nova, Llama 4), the service processes not only text but also tables, charts, and images within documents. Answers can reference the original visual content as their source.
How much does Amazon Bedrock Knowledge Bases cost?
There is no separate charge for the knowledge base feature itself. Costs consist of embedding model invocations (during indexing and each query), the cost of the chosen vector store (e.g. OpenSearch Serverless OCU hours or the lower S3 Vectors storage and query prices), and foundation model inference costs for answer generation. Billing is fully usage-based.
Can I customize the chunking strategy?
Yes, Bedrock Knowledge Bases offers several chunking strategies: fixed-size chunking (defined token count per chunk), semantic chunking (splitting based on content), hierarchical chunking (nested chunks for different granularity levels), and custom chunking via Lambda functions.
What is the difference between a Managed and a Customer-managed Knowledge Base?
With a Managed Knowledge Base, AWS fully handles ingestion, indexing, storage, and retrieval, and adds capabilities such as agentic retrieval and native integration with AgentCore Gateway. With a Customer-managed Knowledge Base, you choose and configure the vector store yourself and have full control over ingestion and indexing. AWS recommends the managed option for most new projects.
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