RAG Architecture in Bedrock — Knowledge Bases and RAG

Ground Responses in Enterprise Data

RAG combines retrieval and generation so model answers are grounded in your approved documentation. Amazon Bedrock Knowledge Bases unifies vector, lexical, graph, and multimodal retrieval architectures:

• Managed Vector & Enterprise Search Backends - store and query embeddings in Amazon OpenSearch Serverless, Aurora PostgreSQL (pgvector), Pinecone, or connect directly to an Amazon Kendra GenAI Index with 40+ enterprise SaaS connectors (SharePoint, Confluence, Salesforce) and document-level ACL enforcement.
• GraphRAG with Amazon Neptune Analytics - automatically extracts entities and relationships from unstructured documents into an in-memory Neptune Analytics property graph, combining vector similarity with multi-hop graph traversal to answer cross-document relational questions.
• Multimodal Ingestion with Bedrock Data Automation (BDA) - natively parses complex PDFs, tables, charts, images, audio transcripts, and video scenes into structured chunks without custom OCR/ETL pipelines.
• Native Reranking - refines retrieved candidates via cross-encoder models (Cohere Rerank 3.5 and Amazon Rerank 1.0) before synthesis.

Single-Call Grounded Synthesis (RetrieveAndGenerate)

kb_client = boto3.client("bedrock-agent-runtime", region_name="us-east-1")
resp = kb_client.retrieve_and_generate(
    input={"text": "Which services depend on the EU payment ledger?"},
    retrieveAndGenerateConfiguration={
        "type": "KNOWLEDGE_BASE",
        "knowledgeBaseConfiguration": {
            "knowledgeBaseId": "KB772819",
            "modelArn": "arn:aws:bedrock:us-east-1:123456789012:inference-profile/us.anthropic.claude-sonnet-5-5",
        },
    },
)
print(resp["output"]["text"], resp["citations"])

Managed Knowledge Bases also integrate natively with AgentCore Gateway, so any MCP-compatible agent framework (such as Strands Agents) can discover and query your knowledge base as a tool with zero glue code.