Advanced vector database architectures

Design vector storage for semantic retrieval.

Possible AWS approaches include:

• Amazon Bedrock Knowledge Bases
• Amazon OpenSearch Service
• Amazon RDS / Aurora with vector capabilities
• Amazon DynamoDB alongside vector storage and metadata
• Amazon S3 as source document storage

Traditional keyword search may fail when the user and document use different wording.

Vector search represents semantic meaning numerically.

S3
 │
 ├── Documents
 └── Metadata
       ↓
   Chunking
       ↓
   Embeddings
       ↓
 Vector Store
       ↓
 Retrieval
       ↓
    Bedrock

Useful when documents contain:

• sections
• subsections
• chapters
• policies
• product categories

The vector store is not the source of truth.

Usually:

Source of Truth = Documents / enterprise system
Vector Store = Retrieval index

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