What is Amazon Bedrock Knowledge Bases?
Amazon Bedrock Knowledge Bases is a fully managed RAG-as-a-Service (Retrieval-Augmented Generation) from AWS. The service allows you to seamlessly integrate enterprise-specific documents and data into foundation models without building and operating 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 queries.
The ingestion process is fully automated: Bedrock Knowledge Bases splits documents into chunks according to configurable strategies, converts them into vectors using an embedding model (e.g., Amazon Titan Embeddings, Cohere Embed), and stores them in the chosen vector store. Supported vector databases include Amazon OpenSearch Serverless, Aurora PostgreSQL with pgvector, Pinecone, Redis Enterprise Cloud, and MongoDB Atlas. For each query, the most relevant chunks are retrieved semantically and passed as context to the foundation model.
Bedrock Knowledge Bases can be directly connected to Amazon Bedrock Agents, allowing AI agents to automatically access enterprise knowledge when needed. For more precise results, the service offers metadata filtering to restrict searches to specific document types, time periods, or departments. Guardrails integration enables filtering of sensitive information in search results.
innFactory supports you in designing and implementing RAG architectures based on Amazon Bedrock Knowledge Bases, from data source integration and choosing the optimal chunking strategy to production operations and continuous quality assurance of 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.
Which vector databases does Bedrock Knowledge Bases support?
Bedrock Knowledge Bases supports Amazon OpenSearch Serverless (recommended), Amazon Aurora PostgreSQL with pgvector, Pinecone, Redis Enterprise Cloud, and MongoDB Atlas as vector stores. For new projects, we recommend OpenSearch Serverless as it is fully managed and integrates seamlessly with other AWS services.
Which data sources can I connect?
Currently supported data sources include Amazon S3 (PDF, Word, HTML, CSV, JSON, Markdown), Confluence, SharePoint, Salesforce, and a web crawler. Documents are automatically split into chunks, converted into vectors, and stored in the configured vector store.
How much does Amazon Bedrock Knowledge Bases cost?
Costs consist of: embedding model invocations (during indexing and each query), vector store storage costs (e.g., OpenSearch Serverless OCU hours), and foundation model inference costs for answer generation. Query-only costs without generation are often significantly lower than full LLM requests in many scenarios.
Can I customize the chunking strategy?
Yes, Bedrock Knowledge Bases offers several chunking strategies: fixed-size chunking (defined token count per chunk), semantic chunking (intelligent splitting based on content), hierarchical chunking (nested chunks for different granularity levels), and custom chunking via Lambda functions.