What is Amazon Bedrock Knowledge Bases?
Amazon Bedrock Knowledge Bases is a fully managed RAG service (Retrieval-Augmented Generation) from AWS. The service integrates enterprise-specific documents and data into foundation models without you having 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.
The ingestion process is automated: Bedrock Knowledge Bases splits documents into chunks according to configurable strategies, converts them into vectors using an embedding model (e.g. Amazon Titan Text Embeddings V2 or Cohere Embed v3), and stores them 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
- 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 (generally available since December 2025, up to 90 percent cheaper per AWS), 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 (generally available since March 2025).
- 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 or vision foundation models, including source references to visual content.
- Precise retrieval: reranking models, metadata filtering, and Guardrails integration increase relevance and filter sensitive information out of results.
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: Bedrock Agents use the knowledge base 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 S3 Vectors you can cut the storage and query costs of large vector datasets by up to 90 percent according to AWS.
- Data sovereignty: runs in EU regions (Frankfurt, Ireland, London, Paris, Stockholm) and a 99.9 percent SLA for Amazon Bedrock.
- Verifiable answers: source citations and reranking increase trust and traceability, while Guardrails filter sensitive content.
- Deep AWS integration: seamless connection with Bedrock Agents, 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.